CarLasso_Network(metatransciptome)
Analysis 1
Figure 1
library(CARlasso)
library(dplyr)## Warning: package 'dplyr' was built under R version 4.3.1##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionset.seed(42)
changed_col<-read.csv("metatransciptome/tax_10.csv")
changed_col$DO_mg_L <- ifelse(changed_col$DO_mg_L < 0, 0, changed_col$DO_mg_L)
last_10_cols <- tail(names(changed_col), 10)
changed_col <- changed_col%>%
mutate(across(all_of(last_10_cols), ~scale(., center = min(.), scale = max(.) - min(.))[,1]))
otu_res <- CARlasso(p__Proteobacteria_1+p__Verrucomicrobiota_2+p__Bacteroidota_3+p__Proteobacteria_4+p__Proteobacteria_5+p__Proteobacteria_6+ p__Verrucomicrobiota_7+p__Bacteroidota_8+p__Actinobacteriota_9+p__Proteobacteria_10~depth+wtemp_in_celsius+specific_conductivity+chlorophyll_RFU+phycocyanin_RFU+ Dissolved_organic_matter_RFU+Turbidity_RFU+DO_mg_L+PH, data = changed_col, adaptive = TRUE, link="log", n_iter = 5000, n_burn_in = 1000, thin_by = 10)## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## doneotu_res <- horseshoe(otu_res)
genomes_list <- c(
'Ga0485157_metabat1.059',
'Ga0485167_maxbin.109',
'Ga0485162_maxbin.089',
'Ga0485161_maxbin.110',
'Ga0485161_maxbin.075',
'Ga0485157_metabat1.036',
'Ga0485165_metabat2_ours.012_sub',
'Ga0485169_maxbin.201_sub',
'Ga0485172_maxbin.081_sub',
'Ga0485168_maxbin.153'
)
# Load the taxonomy data
taxonomy_file_path <- 'Fw_ lake mendota data/MAG_taxonomy.tsv'
taxonomy_df <- read.csv(taxonomy_file_path, sep='\t')
# Load the transcriptome count data
t_count_file_path <- 'metatransciptome_count.csv'
t_count_df <- read.csv(t_count_file_path)
# Create a list of maps for each taxonomy level
genome_to_domain_map <- setNames(taxonomy_df$Domain, taxonomy_df$Genome)
genome_to_phylum_map <- setNames(taxonomy_df$Phylum, taxonomy_df$Genome)
genome_to_class_map <- setNames(taxonomy_df$Class, taxonomy_df$Genome)
genome_to_order_map <- setNames(taxonomy_df$Order, taxonomy_df$Genome)
genome_to_family_map <- setNames(taxonomy_df$Family, taxonomy_df$Genome)
genome_to_genus_map <- setNames(taxonomy_df$Genus, taxonomy_df$Genome)
genome_to_species_map <- setNames(taxonomy_df$Species, taxonomy_df$Genome)
# Extract taxonomy for each genome in your list
domain_names <- sapply(genomes_list, function(genome) genome_to_domain_map[[genome]])
phylum_names <- sapply(genomes_list, function(genome) genome_to_phylum_map[[genome]])
class_names <- sapply(genomes_list, function(genome) genome_to_class_map[[genome]])
order_names <- sapply(genomes_list, function(genome) genome_to_order_map[[genome]])
family_names <- sapply(genomes_list, function(genome) genome_to_family_map[[genome]])
genus_names <- sapply(genomes_list, function(genome) genome_to_genus_map[[genome]])
species_names <- sapply(genomes_list, function(genome) genome_to_species_map[[genome]])
new_column_names <- paste(phylum_names, seq_along(genomes_list), sep="_")
# Create the reference table with all levels
reference_table <- data.frame(
Original_Genome = genomes_list,
Node_Name = new_column_names,
Domain = domain_names,
Phylum = phylum_names,
Class = class_names,
Order = order_names,
Family = family_names,
Genus = genus_names,
Species = species_names
)
plot(otu_res)
Network of Top10 OTUs with All Environmental Features
Table 1
print(reference_table)## Original_Genome
## Ga0485157_metabat1.059 Ga0485157_metabat1.059
## Ga0485167_maxbin.109 Ga0485167_maxbin.109
## Ga0485162_maxbin.089 Ga0485162_maxbin.089
## Ga0485161_maxbin.110 Ga0485161_maxbin.110
## Ga0485161_maxbin.075 Ga0485161_maxbin.075
## Ga0485157_metabat1.036 Ga0485157_metabat1.036
## Ga0485165_metabat2_ours.012_sub Ga0485165_metabat2_ours.012_sub
## Ga0485169_maxbin.201_sub Ga0485169_maxbin.201_sub
## Ga0485172_maxbin.081_sub Ga0485172_maxbin.081_sub
## Ga0485168_maxbin.153 Ga0485168_maxbin.153
## Node_Name Domain
## Ga0485157_metabat1.059 p__Proteobacteria_1 d__Bacteria
## Ga0485167_maxbin.109 p__Verrucomicrobiota_2 d__Bacteria
## Ga0485162_maxbin.089 p__Bacteroidota_3 d__Bacteria
## Ga0485161_maxbin.110 p__Proteobacteria_4 d__Bacteria
## Ga0485161_maxbin.075 p__Proteobacteria_5 d__Bacteria
## Ga0485157_metabat1.036 p__Proteobacteria_6 d__Bacteria
## Ga0485165_metabat2_ours.012_sub p__Verrucomicrobiota_7 d__Bacteria
## Ga0485169_maxbin.201_sub p__Bacteroidota_8 d__Bacteria
## Ga0485172_maxbin.081_sub p__Actinobacteriota_9 d__Bacteria
## Ga0485168_maxbin.153 p__Proteobacteria_10 d__Bacteria
## Phylum Class
## Ga0485157_metabat1.059 p__Proteobacteria c__Gammaproteobacteria
## Ga0485167_maxbin.109 p__Verrucomicrobiota c__Kiritimatiellae
## Ga0485162_maxbin.089 p__Bacteroidota c__Bacteroidia
## Ga0485161_maxbin.110 p__Proteobacteria c__Gammaproteobacteria
## Ga0485161_maxbin.075 p__Proteobacteria c__Gammaproteobacteria
## Ga0485157_metabat1.036 p__Proteobacteria c__Gammaproteobacteria
## Ga0485165_metabat2_ours.012_sub p__Verrucomicrobiota c__Kiritimatiellae
## Ga0485169_maxbin.201_sub p__Bacteroidota c__Bacteroidia
## Ga0485172_maxbin.081_sub p__Actinobacteriota c__Actinomycetia
## Ga0485168_maxbin.153 p__Proteobacteria c__Gammaproteobacteria
## Order Family
## Ga0485157_metabat1.059 o__Burkholderiales f__Rhodocyclaceae
## Ga0485167_maxbin.109 o__RFP12 f__UBA1067
## Ga0485162_maxbin.089 o__Chitinophagales f__Chitinophagaceae
## Ga0485161_maxbin.110 o__Burkholderiales f__Rhodocyclaceae
## Ga0485161_maxbin.075 o__Burkholderiales f__Thiobacillaceae
## Ga0485157_metabat1.036 o__Burkholderiales f__Rhodocyclaceae
## Ga0485165_metabat2_ours.012_sub o__RFP12 f__JAAYNR01
## Ga0485169_maxbin.201_sub o__Bacteroidales f__Prolixibacteraceae
## Ga0485172_maxbin.081_sub o__Nanopelagicales f__Nanopelagicaceae
## Ga0485168_maxbin.153 o__Burkholderiales f__Methylophilaceae
## Genus Species
## Ga0485157_metabat1.059 g__Sulfuritalea s__
## Ga0485167_maxbin.109 g__CAIZQW01 s__CAIZQW01 sp903935195
## Ga0485162_maxbin.089 g__Sediminibacterium s__
## Ga0485161_maxbin.110 g__ s__
## Ga0485161_maxbin.075 g__PFJX01 s__
## Ga0485157_metabat1.036 g__Sulfuritalea s__
## Ga0485165_metabat2_ours.012_sub g__ s__
## Ga0485169_maxbin.201_sub g__Draconibacterium s__
## Ga0485172_maxbin.081_sub g__Planktophila s__
## Ga0485168_maxbin.153 g__Methylotenera s__Analysis 2
Figure 2
library(CARlasso)
set.seed(42)
changed_col<-read.csv("metatransciptome/tax_15.csv")
changed_col$DO_mg_L <- ifelse(changed_col$DO_mg_L < 0, 0, changed_col$DO_mg_L)
last_10_cols <- tail(names(changed_col), 10)
changed_col <- changed_col%>%
mutate(across(all_of(last_10_cols), ~scale(., center = min(.), scale = max(.) - min(.))[,1]))
otu_res <- CARlasso(p__Proteobacteria_1+p__Verrucomicrobiota_2+p__Bacteroidota_3+p__Proteobacteria_4+p__Proteobacteria_5+p__Proteobacteria_6+ p__Verrucomicrobiota_7+p__Bacteroidota_8+p__Actinobacteriota_9+p__Proteobacteria_10+p__Proteobacteria_11+p__Actinobacteriota_12+p__Actinobacteriota_13+p__Verrucomicrobiota_14+p__Desulfobacterota_15~depth+wtemp_in_celsius+specific_conductivity+chlorophyll_RFU+phycocyanin_RFU+Dissolved_organic_matter_RFU+Turbidity_RFU+DO_mg_L+PH, data = changed_col, adaptive = TRUE, link="log", n_iter = 5000, n_burn_in = 1000, thin_by = 10)## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## doneotu_res <- horseshoe(otu_res)
genomes_list <- c(
'Ga0485157_metabat1.059',
'Ga0485167_maxbin.109',
'Ga0485162_maxbin.089',
'Ga0485161_maxbin.110',
'Ga0485161_maxbin.075',
'Ga0485157_metabat1.036',
'Ga0485165_metabat2_ours.012_sub',
'Ga0485169_maxbin.201_sub',
'Ga0485172_maxbin.081_sub',
'Ga0485168_maxbin.153',
'Ga0485172_metabat2_ours.083',
'Ga0485162_maxbin.023',
'Ga0485160_maxbin.092',
'Ga0485158_metabat2_jgi.024',
'Ga0485162_metabat1.001'
)
# Create a list of maps for each taxonomy level
genome_to_domain_map <- setNames(taxonomy_df$Domain, taxonomy_df$Genome)
genome_to_phylum_map <- setNames(taxonomy_df$Phylum, taxonomy_df$Genome)
genome_to_class_map <- setNames(taxonomy_df$Class, taxonomy_df$Genome)
genome_to_order_map <- setNames(taxonomy_df$Order, taxonomy_df$Genome)
genome_to_family_map <- setNames(taxonomy_df$Family, taxonomy_df$Genome)
genome_to_genus_map <- setNames(taxonomy_df$Genus, taxonomy_df$Genome)
genome_to_species_map <- setNames(taxonomy_df$Species, taxonomy_df$Genome)
# Extract taxonomy for each genome in your list
domain_names <- sapply(genomes_list, function(genome) genome_to_domain_map[[genome]])
phylum_names <- sapply(genomes_list, function(genome) genome_to_phylum_map[[genome]])
class_names <- sapply(genomes_list, function(genome) genome_to_class_map[[genome]])
order_names <- sapply(genomes_list, function(genome) genome_to_order_map[[genome]])
family_names <- sapply(genomes_list, function(genome) genome_to_family_map[[genome]])
genus_names <- sapply(genomes_list, function(genome) genome_to_genus_map[[genome]])
species_names <- sapply(genomes_list, function(genome) genome_to_species_map[[genome]])
new_column_names <- paste(phylum_names, seq_along(genomes_list), sep="_")
# Create the reference table with all levels
reference_table <- data.frame(
Original_Genome = genomes_list,
Node_Name = new_column_names,
Domain = domain_names,
Phylum = phylum_names,
Class = class_names,
Order = order_names,
Family = family_names,
Genus = genus_names,
Species = species_names
)
plot(otu_res)
Table 2
print(reference_table)## Original_Genome
## Ga0485157_metabat1.059 Ga0485157_metabat1.059
## Ga0485167_maxbin.109 Ga0485167_maxbin.109
## Ga0485162_maxbin.089 Ga0485162_maxbin.089
## Ga0485161_maxbin.110 Ga0485161_maxbin.110
## Ga0485161_maxbin.075 Ga0485161_maxbin.075
## Ga0485157_metabat1.036 Ga0485157_metabat1.036
## Ga0485165_metabat2_ours.012_sub Ga0485165_metabat2_ours.012_sub
## Ga0485169_maxbin.201_sub Ga0485169_maxbin.201_sub
## Ga0485172_maxbin.081_sub Ga0485172_maxbin.081_sub
## Ga0485168_maxbin.153 Ga0485168_maxbin.153
## Ga0485172_metabat2_ours.083 Ga0485172_metabat2_ours.083
## Ga0485162_maxbin.023 Ga0485162_maxbin.023
## Ga0485160_maxbin.092 Ga0485160_maxbin.092
## Ga0485158_metabat2_jgi.024 Ga0485158_metabat2_jgi.024
## Ga0485162_metabat1.001 Ga0485162_metabat1.001
## Node_Name Domain
## Ga0485157_metabat1.059 p__Proteobacteria_1 d__Bacteria
## Ga0485167_maxbin.109 p__Verrucomicrobiota_2 d__Bacteria
## Ga0485162_maxbin.089 p__Bacteroidota_3 d__Bacteria
## Ga0485161_maxbin.110 p__Proteobacteria_4 d__Bacteria
## Ga0485161_maxbin.075 p__Proteobacteria_5 d__Bacteria
## Ga0485157_metabat1.036 p__Proteobacteria_6 d__Bacteria
## Ga0485165_metabat2_ours.012_sub p__Verrucomicrobiota_7 d__Bacteria
## Ga0485169_maxbin.201_sub p__Bacteroidota_8 d__Bacteria
## Ga0485172_maxbin.081_sub p__Actinobacteriota_9 d__Bacteria
## Ga0485168_maxbin.153 p__Proteobacteria_10 d__Bacteria
## Ga0485172_metabat2_ours.083 p__Proteobacteria_11 d__Bacteria
## Ga0485162_maxbin.023 p__Actinobacteriota_12 d__Bacteria
## Ga0485160_maxbin.092 p__Actinobacteriota_13 d__Bacteria
## Ga0485158_metabat2_jgi.024 p__Verrucomicrobiota_14 d__Bacteria
## Ga0485162_metabat1.001 p__Desulfobacterota_15 d__Bacteria
## Phylum Class
## Ga0485157_metabat1.059 p__Proteobacteria c__Gammaproteobacteria
## Ga0485167_maxbin.109 p__Verrucomicrobiota c__Kiritimatiellae
## Ga0485162_maxbin.089 p__Bacteroidota c__Bacteroidia
## Ga0485161_maxbin.110 p__Proteobacteria c__Gammaproteobacteria
## Ga0485161_maxbin.075 p__Proteobacteria c__Gammaproteobacteria
## Ga0485157_metabat1.036 p__Proteobacteria c__Gammaproteobacteria
## Ga0485165_metabat2_ours.012_sub p__Verrucomicrobiota c__Kiritimatiellae
## Ga0485169_maxbin.201_sub p__Bacteroidota c__Bacteroidia
## Ga0485172_maxbin.081_sub p__Actinobacteriota c__Actinomycetia
## Ga0485168_maxbin.153 p__Proteobacteria c__Gammaproteobacteria
## Ga0485172_metabat2_ours.083 p__Proteobacteria c__Gammaproteobacteria
## Ga0485162_maxbin.023 p__Actinobacteriota c__Actinomycetia
## Ga0485160_maxbin.092 p__Actinobacteriota c__Actinomycetia
## Ga0485158_metabat2_jgi.024 p__Verrucomicrobiota c__Lentisphaeria
## Ga0485162_metabat1.001 p__Desulfobacterota c__Desulfobacteria
## Order Family
## Ga0485157_metabat1.059 o__Burkholderiales f__Rhodocyclaceae
## Ga0485167_maxbin.109 o__RFP12 f__UBA1067
## Ga0485162_maxbin.089 o__Chitinophagales f__Chitinophagaceae
## Ga0485161_maxbin.110 o__Burkholderiales f__Rhodocyclaceae
## Ga0485161_maxbin.075 o__Burkholderiales f__Thiobacillaceae
## Ga0485157_metabat1.036 o__Burkholderiales f__Rhodocyclaceae
## Ga0485165_metabat2_ours.012_sub o__RFP12 f__JAAYNR01
## Ga0485169_maxbin.201_sub o__Bacteroidales f__Prolixibacteraceae
## Ga0485172_maxbin.081_sub o__Nanopelagicales f__Nanopelagicaceae
## Ga0485168_maxbin.153 o__Burkholderiales f__Methylophilaceae
## Ga0485172_metabat2_ours.083 o__Burkholderiales f__Methylophilaceae
## Ga0485162_maxbin.023 o__Nanopelagicales f__Nanopelagicaceae
## Ga0485160_maxbin.092 o__Nanopelagicales f__Nanopelagicaceae
## Ga0485158_metabat2_jgi.024 o__Victivallales f__GWF2-50-93
## Ga0485162_metabat1.001 o__Desulfobacterales f__Desulfatirhabdiaceae
## Genus
## Ga0485157_metabat1.059 g__Sulfuritalea
## Ga0485167_maxbin.109 g__CAIZQW01
## Ga0485162_maxbin.089 g__Sediminibacterium
## Ga0485161_maxbin.110 g__
## Ga0485161_maxbin.075 g__PFJX01
## Ga0485157_metabat1.036 g__Sulfuritalea
## Ga0485165_metabat2_ours.012_sub g__
## Ga0485169_maxbin.201_sub g__Draconibacterium
## Ga0485172_maxbin.081_sub g__Planktophila
## Ga0485168_maxbin.153 g__Methylotenera
## Ga0485172_metabat2_ours.083 g__Methylopumilus
## Ga0485162_maxbin.023 g__Planktophila
## Ga0485160_maxbin.092 g__Nanopelagicus
## Ga0485158_metabat2_jgi.024 g__
## Ga0485162_metabat1.001 g__RAAP-1
## Species
## Ga0485157_metabat1.059 s__
## Ga0485167_maxbin.109 s__CAIZQW01 sp903935195
## Ga0485162_maxbin.089 s__
## Ga0485161_maxbin.110 s__
## Ga0485161_maxbin.075 s__
## Ga0485157_metabat1.036 s__
## Ga0485165_metabat2_ours.012_sub s__
## Ga0485169_maxbin.201_sub s__
## Ga0485172_maxbin.081_sub s__
## Ga0485168_maxbin.153 s__
## Ga0485172_metabat2_ours.083 s__Methylopumilus universalis
## Ga0485162_maxbin.023 s__
## Ga0485160_maxbin.092 s__Nanopelagicus sp000383815
## Ga0485158_metabat2_jgi.024 s__
## Ga0485162_metabat1.001 s__Analysis 3
Figure 3
library(CARlasso)
set.seed(42)
changed_col<-read.csv("metatransciptome/tax_Betweenness.csv")
changed_col$DO_mg_L <- ifelse(changed_col$DO_mg_L < 0, 0, changed_col$DO_mg_L)
last_10_cols <- tail(names(changed_col), 10)
changed_col <- changed_col%>%
mutate(across(all_of(last_10_cols), ~scale(., center = min(.), scale = max(.) - min(.))[,1]))
otu_res <- CARlasso(p__Desulfobacterota_1
+p__Planctomycetota_2
+p__Proteobacteria_3
+p__Chloroflexota_4
+p__Bacteroidota_5
+p__Verrucomicrobiota_6
+p__Proteobacteria_7
+p__Actinobacteriota_8
+p__Krumholzibacteriota_9
+p__Armatimonadota_10
+p__Bacteroidota_11
+p__Verrucomicrobiota_12
+p__Actinobacteriota_13
+p__Firmicutes_A_14
+p__Desulfobacterota_15~depth+wtemp_in_celsius+specific_conductivity+chlorophyll_RFU+phycocyanin_RFU+Dissolved_organic_matter_RFU+Turbidity_RFU+DO_mg_L+PH, data = changed_col, adaptive = TRUE, link="log", n_iter = 5000, n_burn_in = 1000, thin_by = 10)## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## doneotu_res <- horseshoe(otu_res)
genomes_list <- c(
"Ga0485158_maxbin.120_sub",
"Ga0485170_maxbin.090",
"Ga0485159_metabat1.040",
"Ga0485167_metabat2_ours.163",
"Ga0485166_metabat2_ours.038",
"Ga0485162_metabat2_ours.116",
"Ga0485162_metabat2_ours.050",
"Ga0485163_metabat1.181",
"Ga0485158_metabat2_ours.098",
"Ga0485169_metabat2_ours.035",
"Ga0485159_metabat2_ours.130_sub",
"Ga0485163_metabat1.115_sub",
"Ga0485159_maxbin.025",
"Ga0485161_metabat1.096",
"Ga0485167_metabat1.127_sub"
)
# Create a list of maps for each taxonomy level
genome_to_domain_map <- setNames(taxonomy_df$Domain, taxonomy_df$Genome)
genome_to_phylum_map <- setNames(taxonomy_df$Phylum, taxonomy_df$Genome)
genome_to_class_map <- setNames(taxonomy_df$Class, taxonomy_df$Genome)
genome_to_order_map <- setNames(taxonomy_df$Order, taxonomy_df$Genome)
genome_to_family_map <- setNames(taxonomy_df$Family, taxonomy_df$Genome)
genome_to_genus_map <- setNames(taxonomy_df$Genus, taxonomy_df$Genome)
genome_to_species_map <- setNames(taxonomy_df$Species, taxonomy_df$Genome)
# Extract taxonomy for each genome in your list
domain_names <- sapply(genomes_list, function(genome) genome_to_domain_map[[genome]])
phylum_names <- sapply(genomes_list, function(genome) genome_to_phylum_map[[genome]])
class_names <- sapply(genomes_list, function(genome) genome_to_class_map[[genome]])
order_names <- sapply(genomes_list, function(genome) genome_to_order_map[[genome]])
family_names <- sapply(genomes_list, function(genome) genome_to_family_map[[genome]])
genus_names <- sapply(genomes_list, function(genome) genome_to_genus_map[[genome]])
species_names <- sapply(genomes_list, function(genome) genome_to_species_map[[genome]])
new_column_names <- paste(phylum_names, seq_along(genomes_list), sep="_")
# Create the reference table with all levels
reference_table <- data.frame(
Original_Genome = genomes_list,
Node_Name = new_column_names,
Domain = domain_names,
Phylum = phylum_names,
Class = class_names,
Order = order_names,
Family = family_names,
Genus = genus_names,
Species = species_names
)
plot(otu_res)
Table 3
print(reference_table)## Original_Genome
## Ga0485158_maxbin.120_sub Ga0485158_maxbin.120_sub
## Ga0485170_maxbin.090 Ga0485170_maxbin.090
## Ga0485159_metabat1.040 Ga0485159_metabat1.040
## Ga0485167_metabat2_ours.163 Ga0485167_metabat2_ours.163
## Ga0485166_metabat2_ours.038 Ga0485166_metabat2_ours.038
## Ga0485162_metabat2_ours.116 Ga0485162_metabat2_ours.116
## Ga0485162_metabat2_ours.050 Ga0485162_metabat2_ours.050
## Ga0485163_metabat1.181 Ga0485163_metabat1.181
## Ga0485158_metabat2_ours.098 Ga0485158_metabat2_ours.098
## Ga0485169_metabat2_ours.035 Ga0485169_metabat2_ours.035
## Ga0485159_metabat2_ours.130_sub Ga0485159_metabat2_ours.130_sub
## Ga0485163_metabat1.115_sub Ga0485163_metabat1.115_sub
## Ga0485159_maxbin.025 Ga0485159_maxbin.025
## Ga0485161_metabat1.096 Ga0485161_metabat1.096
## Ga0485167_metabat1.127_sub Ga0485167_metabat1.127_sub
## Node_Name Domain
## Ga0485158_maxbin.120_sub p__Desulfobacterota_1 d__Bacteria
## Ga0485170_maxbin.090 p__Planctomycetota_2 d__Bacteria
## Ga0485159_metabat1.040 p__Proteobacteria_3 d__Bacteria
## Ga0485167_metabat2_ours.163 p__Chloroflexota_4 d__Bacteria
## Ga0485166_metabat2_ours.038 p__Bacteroidota_5 d__Bacteria
## Ga0485162_metabat2_ours.116 p__Verrucomicrobiota_6 d__Bacteria
## Ga0485162_metabat2_ours.050 p__Proteobacteria_7 d__Bacteria
## Ga0485163_metabat1.181 p__Actinobacteriota_8 d__Bacteria
## Ga0485158_metabat2_ours.098 p__Krumholzibacteriota_9 d__Bacteria
## Ga0485169_metabat2_ours.035 p__Armatimonadota_10 d__Bacteria
## Ga0485159_metabat2_ours.130_sub p__Bacteroidota_11 d__Bacteria
## Ga0485163_metabat1.115_sub p__Verrucomicrobiota_12 d__Bacteria
## Ga0485159_maxbin.025 p__Actinobacteriota_13 d__Bacteria
## Ga0485161_metabat1.096 p__Firmicutes_A_14 d__Bacteria
## Ga0485167_metabat1.127_sub p__Desulfobacterota_15 d__Bacteria
## Phylum Class
## Ga0485158_maxbin.120_sub p__Desulfobacterota c__Desulfomonilia
## Ga0485170_maxbin.090 p__Planctomycetota c__Phycisphaerae
## Ga0485159_metabat1.040 p__Proteobacteria c__Gammaproteobacteria
## Ga0485167_metabat2_ours.163 p__Chloroflexota c__Anaerolineae
## Ga0485166_metabat2_ours.038 p__Bacteroidota c__Bacteroidia
## Ga0485162_metabat2_ours.116 p__Verrucomicrobiota c__Kiritimatiellae
## Ga0485162_metabat2_ours.050 p__Proteobacteria c__Gammaproteobacteria
## Ga0485163_metabat1.181 p__Actinobacteriota c__Thermoleophilia
## Ga0485158_metabat2_ours.098 p__Krumholzibacteriota c__Krumholzibacteria
## Ga0485169_metabat2_ours.035 p__Armatimonadota c__UBA5829
## Ga0485159_metabat2_ours.130_sub p__Bacteroidota c__Bacteroidia
## Ga0485163_metabat1.115_sub p__Verrucomicrobiota c__Verrucomicrobiae
## Ga0485159_maxbin.025 p__Actinobacteriota c__Actinomycetia
## Ga0485161_metabat1.096 p__Firmicutes_A c__Clostridia
## Ga0485167_metabat1.127_sub p__Desulfobacterota c__Desulfobacteria
## Order Family
## Ga0485158_maxbin.120_sub o__Desulfomonilales f__Desulfomonilaceae
## Ga0485170_maxbin.090 o__Phycisphaerales f__SM1A02
## Ga0485159_metabat1.040 o__Steroidobacterales f__Steroidobacteraceae
## Ga0485167_metabat2_ours.163 o__Anaerolineales f__DRKV01
## Ga0485166_metabat2_ours.038 o__Bacteroidales f__FEN-979
## Ga0485162_metabat2_ours.116 o__Kiritimatiellales f__Pontiellaceae
## Ga0485162_metabat2_ours.050 o__Burkholderiales f__Rhodocyclaceae
## Ga0485163_metabat1.181 o__Gaiellales f__F1-60-MAGs149
## Ga0485158_metabat2_ours.098 o__LZORAL124-64-63 f__LZORAL124-64-63
## Ga0485169_metabat2_ours.035 o__UBA5829 f__UBA5829
## Ga0485159_metabat2_ours.130_sub o__Flavobacteriales f__UA16
## Ga0485163_metabat1.115_sub o__Opitutales f__Opitutaceae
## Ga0485159_maxbin.025 o__Nanopelagicales f__Nanopelagicaceae
## Ga0485161_metabat1.096 o__Saccharofermentanales f__UBA5734
## Ga0485167_metabat1.127_sub o__Desulfobacterales f__Desulfobacteraceae
## Genus Species
## Ga0485158_maxbin.120_sub g__CAJBEZ01 s__
## Ga0485170_maxbin.090 g__UBA966 s__
## Ga0485159_metabat1.040 g__UBA964 s__
## Ga0485167_metabat2_ours.163 g__ s__
## Ga0485166_metabat2_ours.038 g__CAIVAT01 s__
## Ga0485162_metabat2_ours.116 g__UBA5540 s__
## Ga0485162_metabat2_ours.050 g__ s__
## Ga0485163_metabat1.181 g__F1-60-MAGs149 s__
## Ga0485158_metabat2_ours.098 g__CAINDZ01 s__
## Ga0485169_metabat2_ours.035 g__UBA5829 s__
## Ga0485159_metabat2_ours.130_sub g__ s__
## Ga0485163_metabat1.115_sub g__ER46 s__
## Ga0485159_maxbin.025 g__SYAN01 s__
## Ga0485161_metabat1.096 g__CAILLO01 s__
## Ga0485167_metabat1.127_sub g__Desulfobacula s__Analysis 4
Figure 4
library(CARlasso)
set.seed(42)
changed_col<-read.csv("metatransciptome/tax_Closeness.csv")
changed_col$DO_mg_L <- ifelse(changed_col$DO_mg_L < 0, 0, changed_col$DO_mg_L)
last_10_cols <- tail(names(changed_col), 10)
changed_col <- changed_col%>%
mutate(across(all_of(last_10_cols), ~scale(., center = min(.), scale = max(.) - min(.))[,1]))
otu_res <- CARlasso(p__Verrucomicrobiota_1+p__Actinobacteriota_2
+p__Bacteroidota_3
+p__Bacteroidota_4
+p__Bacteroidota_5
+p__Actinobacteriota_6
+p__Bacteroidota_7
+p__Bacteroidota_8
+p__Actinobacteriota_9
+p__Proteobacteria_10
+p__Planctomycetota_11
+p__Proteobacteria_12
+p__Proteobacteria_13
+p__Bacteroidota_14
+p__Verrucomicrobiota_15~depth+wtemp_in_celsius+specific_conductivity+chlorophyll_RFU+phycocyanin_RFU+Dissolved_organic_matter_RFU+Turbidity_RFU+DO_mg_L+PH, data = changed_col, adaptive = TRUE, link="log", n_iter = 5000, n_burn_in = 1000, thin_by = 10)## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## doneotu_res <- horseshoe(otu_res)
genomes_list <- c(
"Ga0485157_metabat1.069",
"Ga0485157_metabat2_ours.025",
"Ga0485157_metabat2_ours.091",
"Ga0485158_metabat2_ours.184",
"Ga0485159_maxbin.015",
"Ga0485160_maxbin.155_sub",
"Ga0485160_metabat2_ours.051",
"Ga0485160_metabat2_ours.071",
"Ga0485161_metabat1.109_sub",
"Ga0485162_metabat2_ours.036",
"Ga0485163_maxbin.002_sub",
"Ga0485163_maxbin.104_sub",
"Ga0485163_maxbin.129",
"Ga0485163_maxbin.130_sub",
"Ga0485163_maxbin.183_sub"
)
# Create a list of maps for each taxonomy level
genome_to_domain_map <- setNames(taxonomy_df$Domain, taxonomy_df$Genome)
genome_to_phylum_map <- setNames(taxonomy_df$Phylum, taxonomy_df$Genome)
genome_to_class_map <- setNames(taxonomy_df$Class, taxonomy_df$Genome)
genome_to_order_map <- setNames(taxonomy_df$Order, taxonomy_df$Genome)
genome_to_family_map <- setNames(taxonomy_df$Family, taxonomy_df$Genome)
genome_to_genus_map <- setNames(taxonomy_df$Genus, taxonomy_df$Genome)
genome_to_species_map <- setNames(taxonomy_df$Species, taxonomy_df$Genome)
# Extract taxonomy for each genome in your list
domain_names <- sapply(genomes_list, function(genome) genome_to_domain_map[[genome]])
phylum_names <- sapply(genomes_list, function(genome) genome_to_phylum_map[[genome]])
class_names <- sapply(genomes_list, function(genome) genome_to_class_map[[genome]])
order_names <- sapply(genomes_list, function(genome) genome_to_order_map[[genome]])
family_names <- sapply(genomes_list, function(genome) genome_to_family_map[[genome]])
genus_names <- sapply(genomes_list, function(genome) genome_to_genus_map[[genome]])
species_names <- sapply(genomes_list, function(genome) genome_to_species_map[[genome]])
new_column_names <- paste(phylum_names, seq_along(genomes_list), sep="_")
# Create the reference table with all levels
reference_table <- data.frame(
Original_Genome = genomes_list,
Node_Name = new_column_names,
Domain = domain_names,
Phylum = phylum_names,
Class = class_names,
Order = order_names,
Family = family_names,
Genus = genus_names,
Species = species_names
)
plot(otu_res)
Table 4
print(reference_table)## Original_Genome Node_Name
## Ga0485157_metabat1.069 Ga0485157_metabat1.069 p__Verrucomicrobiota_1
## Ga0485157_metabat2_ours.025 Ga0485157_metabat2_ours.025 p__Actinobacteriota_2
## Ga0485157_metabat2_ours.091 Ga0485157_metabat2_ours.091 p__Bacteroidota_3
## Ga0485158_metabat2_ours.184 Ga0485158_metabat2_ours.184 p__Bacteroidota_4
## Ga0485159_maxbin.015 Ga0485159_maxbin.015 p__Bacteroidota_5
## Ga0485160_maxbin.155_sub Ga0485160_maxbin.155_sub p__Actinobacteriota_6
## Ga0485160_metabat2_ours.051 Ga0485160_metabat2_ours.051 p__Bacteroidota_7
## Ga0485160_metabat2_ours.071 Ga0485160_metabat2_ours.071 p__Bacteroidota_8
## Ga0485161_metabat1.109_sub Ga0485161_metabat1.109_sub p__Actinobacteriota_9
## Ga0485162_metabat2_ours.036 Ga0485162_metabat2_ours.036 p__Proteobacteria_10
## Ga0485163_maxbin.002_sub Ga0485163_maxbin.002_sub p__Planctomycetota_11
## Ga0485163_maxbin.104_sub Ga0485163_maxbin.104_sub p__Proteobacteria_12
## Ga0485163_maxbin.129 Ga0485163_maxbin.129 p__Proteobacteria_13
## Ga0485163_maxbin.130_sub Ga0485163_maxbin.130_sub p__Bacteroidota_14
## Ga0485163_maxbin.183_sub Ga0485163_maxbin.183_sub p__Verrucomicrobiota_15
## Domain Phylum
## Ga0485157_metabat1.069 d__Bacteria p__Verrucomicrobiota
## Ga0485157_metabat2_ours.025 d__Bacteria p__Actinobacteriota
## Ga0485157_metabat2_ours.091 d__Bacteria p__Bacteroidota
## Ga0485158_metabat2_ours.184 d__Bacteria p__Bacteroidota
## Ga0485159_maxbin.015 d__Bacteria p__Bacteroidota
## Ga0485160_maxbin.155_sub d__Bacteria p__Actinobacteriota
## Ga0485160_metabat2_ours.051 d__Bacteria p__Bacteroidota
## Ga0485160_metabat2_ours.071 d__Bacteria p__Bacteroidota
## Ga0485161_metabat1.109_sub d__Bacteria p__Actinobacteriota
## Ga0485162_metabat2_ours.036 d__Bacteria p__Proteobacteria
## Ga0485163_maxbin.002_sub d__Bacteria p__Planctomycetota
## Ga0485163_maxbin.104_sub d__Bacteria p__Proteobacteria
## Ga0485163_maxbin.129 d__Bacteria p__Proteobacteria
## Ga0485163_maxbin.130_sub d__Bacteria p__Bacteroidota
## Ga0485163_maxbin.183_sub d__Bacteria p__Verrucomicrobiota
## Class Order
## Ga0485157_metabat1.069 c__Verrucomicrobiae o__Opitutales
## Ga0485157_metabat2_ours.025 c__Actinomycetia o__Nanopelagicales
## Ga0485157_metabat2_ours.091 c__Bacteroidia o__Cytophagales
## Ga0485158_metabat2_ours.184 c__Bacteroidia o__Flavobacteriales
## Ga0485159_maxbin.015 c__Bacteroidia o__Chitinophagales
## Ga0485160_maxbin.155_sub c__Acidimicrobiia o__Acidimicrobiales
## Ga0485160_metabat2_ours.051 c__Bacteroidia o__Chitinophagales
## Ga0485160_metabat2_ours.071 c__Bacteroidia o__Chitinophagales
## Ga0485161_metabat1.109_sub c__Actinomycetia o__Nanopelagicales
## Ga0485162_metabat2_ours.036 c__Gammaproteobacteria o__Burkholderiales
## Ga0485163_maxbin.002_sub c__UBA1135 o__UBA1135
## Ga0485163_maxbin.104_sub c__Alphaproteobacteria o__Caulobacterales
## Ga0485163_maxbin.129 c__Gammaproteobacteria o__Steroidobacterales
## Ga0485163_maxbin.130_sub c__Bacteroidia o__Flavobacteriales
## Ga0485163_maxbin.183_sub c__Verrucomicrobiae o__Pedosphaerales
## Family Genus
## Ga0485157_metabat1.069 f__Opitutaceae g__Tous-C4FEB
## Ga0485157_metabat2_ours.025 f__Nanopelagicaceae g__Planktophila
## Ga0485157_metabat2_ours.091 f__Spirosomaceae g__Aquirufa
## Ga0485158_metabat2_ours.184 f__Flavobacteriaceae g__Flavobacterium
## Ga0485159_maxbin.015 f__UBA10324 g__BJGO01
## Ga0485160_maxbin.155_sub f__Ilumatobacteraceae g__F1-60-MAGs027
## Ga0485160_metabat2_ours.051 f__Chitinophagaceae g__Sediminibacterium
## Ga0485160_metabat2_ours.071 f__Saprospiraceae g__M3007
## Ga0485161_metabat1.109_sub f__Nanopelagicaceae g__Planktophila
## Ga0485162_metabat2_ours.036 f__Burkholderiaceae g__Limnohabitans_A
## Ga0485163_maxbin.002_sub f__GCA-002686595 g__SYGM01
## Ga0485163_maxbin.104_sub f__Hyphomonadaceae g__VFBF01
## Ga0485163_maxbin.129 f__Steroidobacteraceae g__UBA964
## Ga0485163_maxbin.130_sub f__PHOS-HE28 g__PHOS-HE28
## Ga0485163_maxbin.183_sub f__UBA9464 g__CAIWAH01
## Species
## Ga0485157_metabat1.069 s__Tous-C4FEB sp903938935
## Ga0485157_metabat2_ours.025 s__
## Ga0485157_metabat2_ours.091 s__
## Ga0485158_metabat2_ours.184 s__
## Ga0485159_maxbin.015 s__BJGO01 sp014190535
## Ga0485160_maxbin.155_sub s__
## Ga0485160_metabat2_ours.051 s__
## Ga0485160_metabat2_ours.071 s__
## Ga0485161_metabat1.109_sub s__
## Ga0485162_metabat2_ours.036 s__
## Ga0485163_maxbin.002_sub s__
## Ga0485163_maxbin.104_sub s__
## Ga0485163_maxbin.129 s__
## Ga0485163_maxbin.130_sub s__
## Ga0485163_maxbin.183_sub s__Analysis 5
Figure 5
library(CARlasso)
library(dplyr)
set.seed(42)
changed_col<-read.csv("metatransciptome/tax_Page_Rank.csv")
changed_col$DO_mg_L <- ifelse(changed_col$DO_mg_L < 0, 0, changed_col$DO_mg_L)
last_10_cols <- tail(names(changed_col), 10)
changed_col <- changed_col%>%
mutate(across(all_of(last_10_cols), ~scale(., center = min(.), scale = max(.) - min(.))[,1]))
otu_res <- CARlasso(p__Krumholzibacteriota_1
+p__Bacteroidota_2
+p__Verrucomicrobiota_3
+p__Planctomycetota_4
+p__Verrucomicrobiota_5
+p__Planctomycetota_6
+p__Proteobacteria_7
+p__Proteobacteria_8
+p__Bacteroidota_9
+p__Bacteroidota_10
+p__Bacteroidota_11
+p__Actinobacteriota_12
+p__Verrucomicrobiota_13
+p__Proteobacteria_14
+p__Planctomycetota_15~depth+wtemp_in_celsius+specific_conductivity+chlorophyll_RFU+phycocyanin_RFU+Dissolved_organic_matter_RFU+Turbidity_RFU+DO_mg_L+PH, data = changed_col, adaptive = TRUE, link="log", n_iter = 5000, n_burn_in = 1000, thin_by = 10)## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## doneotu_res <- horseshoe(otu_res)
genomes_list <- c(
"Ga0485158_metabat2_ours.098",
"Ga0485166_metabat2_ours.038",
"Ga0485167_metabat2_ours.023",
"Ga0485170_maxbin.090",
"Ga0485161_metabat2_ours.167_sub",
"Ga0485171_maxbin.130_sub",
"Ga0485161_maxbin.110",
"Ga0485164_metabat2_ours.069_sub",
"Ga0485171_metabat1.063",
"Ga0485159_metabat2_ours.079",
"Ga0485171_metabat2_ours.127_sub",
"Ga0485159_metabat2_ours.155_sub",
"Ga0485158_metabat1.076",
"Ga0485171_metabat1.030",
"Ga0485168_metabat2_ours.135_sub"
)
# Create a list of maps for each taxonomy level
genome_to_domain_map <- setNames(taxonomy_df$Domain, taxonomy_df$Genome)
genome_to_phylum_map <- setNames(taxonomy_df$Phylum, taxonomy_df$Genome)
genome_to_class_map <- setNames(taxonomy_df$Class, taxonomy_df$Genome)
genome_to_order_map <- setNames(taxonomy_df$Order, taxonomy_df$Genome)
genome_to_family_map <- setNames(taxonomy_df$Family, taxonomy_df$Genome)
genome_to_genus_map <- setNames(taxonomy_df$Genus, taxonomy_df$Genome)
genome_to_species_map <- setNames(taxonomy_df$Species, taxonomy_df$Genome)
# Extract taxonomy for each genome in your list
domain_names <- sapply(genomes_list, function(genome) genome_to_domain_map[[genome]])
phylum_names <- sapply(genomes_list, function(genome) genome_to_phylum_map[[genome]])
class_names <- sapply(genomes_list, function(genome) genome_to_class_map[[genome]])
order_names <- sapply(genomes_list, function(genome) genome_to_order_map[[genome]])
family_names <- sapply(genomes_list, function(genome) genome_to_family_map[[genome]])
genus_names <- sapply(genomes_list, function(genome) genome_to_genus_map[[genome]])
species_names <- sapply(genomes_list, function(genome) genome_to_species_map[[genome]])
new_column_names <- paste(phylum_names, seq_along(genomes_list), sep="_")
# Create the reference table with all levels
reference_table <- data.frame(
Original_Genome = genomes_list,
Node_Name = new_column_names,
Domain = domain_names,
Phylum = phylum_names,
Class = class_names,
Order = order_names,
Family = family_names,
Genus = genus_names,
Species = species_names
)
plot(otu_res)
Table 5
print(reference_table)# The table Below serves as a reference to the response nodes in the plot!## Original_Genome
## Ga0485158_metabat2_ours.098 Ga0485158_metabat2_ours.098
## Ga0485166_metabat2_ours.038 Ga0485166_metabat2_ours.038
## Ga0485167_metabat2_ours.023 Ga0485167_metabat2_ours.023
## Ga0485170_maxbin.090 Ga0485170_maxbin.090
## Ga0485161_metabat2_ours.167_sub Ga0485161_metabat2_ours.167_sub
## Ga0485171_maxbin.130_sub Ga0485171_maxbin.130_sub
## Ga0485161_maxbin.110 Ga0485161_maxbin.110
## Ga0485164_metabat2_ours.069_sub Ga0485164_metabat2_ours.069_sub
## Ga0485171_metabat1.063 Ga0485171_metabat1.063
## Ga0485159_metabat2_ours.079 Ga0485159_metabat2_ours.079
## Ga0485171_metabat2_ours.127_sub Ga0485171_metabat2_ours.127_sub
## Ga0485159_metabat2_ours.155_sub Ga0485159_metabat2_ours.155_sub
## Ga0485158_metabat1.076 Ga0485158_metabat1.076
## Ga0485171_metabat1.030 Ga0485171_metabat1.030
## Ga0485168_metabat2_ours.135_sub Ga0485168_metabat2_ours.135_sub
## Node_Name Domain
## Ga0485158_metabat2_ours.098 p__Krumholzibacteriota_1 d__Bacteria
## Ga0485166_metabat2_ours.038 p__Bacteroidota_2 d__Bacteria
## Ga0485167_metabat2_ours.023 p__Verrucomicrobiota_3 d__Bacteria
## Ga0485170_maxbin.090 p__Planctomycetota_4 d__Bacteria
## Ga0485161_metabat2_ours.167_sub p__Verrucomicrobiota_5 d__Bacteria
## Ga0485171_maxbin.130_sub p__Planctomycetota_6 d__Bacteria
## Ga0485161_maxbin.110 p__Proteobacteria_7 d__Bacteria
## Ga0485164_metabat2_ours.069_sub p__Proteobacteria_8 d__Bacteria
## Ga0485171_metabat1.063 p__Bacteroidota_9 d__Bacteria
## Ga0485159_metabat2_ours.079 p__Bacteroidota_10 d__Bacteria
## Ga0485171_metabat2_ours.127_sub p__Bacteroidota_11 d__Bacteria
## Ga0485159_metabat2_ours.155_sub p__Actinobacteriota_12 d__Bacteria
## Ga0485158_metabat1.076 p__Verrucomicrobiota_13 d__Bacteria
## Ga0485171_metabat1.030 p__Proteobacteria_14 d__Bacteria
## Ga0485168_metabat2_ours.135_sub p__Planctomycetota_15 d__Bacteria
## Phylum Class
## Ga0485158_metabat2_ours.098 p__Krumholzibacteriota c__Krumholzibacteria
## Ga0485166_metabat2_ours.038 p__Bacteroidota c__Bacteroidia
## Ga0485167_metabat2_ours.023 p__Verrucomicrobiota c__Kiritimatiellae
## Ga0485170_maxbin.090 p__Planctomycetota c__Phycisphaerae
## Ga0485161_metabat2_ours.167_sub p__Verrucomicrobiota c__Kiritimatiellae
## Ga0485171_maxbin.130_sub p__Planctomycetota c__Phycisphaerae
## Ga0485161_maxbin.110 p__Proteobacteria c__Gammaproteobacteria
## Ga0485164_metabat2_ours.069_sub p__Proteobacteria c__Gammaproteobacteria
## Ga0485171_metabat1.063 p__Bacteroidota c__Bacteroidia
## Ga0485159_metabat2_ours.079 p__Bacteroidota c__Bacteroidia
## Ga0485171_metabat2_ours.127_sub p__Bacteroidota c__Bacteroidia
## Ga0485159_metabat2_ours.155_sub p__Actinobacteriota c__Actinomycetia
## Ga0485158_metabat1.076 p__Verrucomicrobiota c__Kiritimatiellae
## Ga0485171_metabat1.030 p__Proteobacteria c__Alphaproteobacteria
## Ga0485168_metabat2_ours.135_sub p__Planctomycetota c__Phycisphaerae
## Order Family
## Ga0485158_metabat2_ours.098 o__LZORAL124-64-63 f__LZORAL124-64-63
## Ga0485166_metabat2_ours.038 o__Bacteroidales f__FEN-979
## Ga0485167_metabat2_ours.023 o__CAIKKV01 f__CAITUY01
## Ga0485170_maxbin.090 o__Phycisphaerales f__SM1A02
## Ga0485161_metabat2_ours.167_sub o__RFP12 f__UBA1067
## Ga0485171_maxbin.130_sub o__Phycisphaerales f__SM1A02
## Ga0485161_maxbin.110 o__Burkholderiales f__Rhodocyclaceae
## Ga0485164_metabat2_ours.069_sub o__Burkholderiales f__Rhodocyclaceae
## Ga0485171_metabat1.063 o__Chitinophagales f__LD1
## Ga0485159_metabat2_ours.079 o__Chitinophagales f__Chitinophagaceae
## Ga0485171_metabat2_ours.127_sub o__Chitinophagales f__Chitinophagaceae
## Ga0485159_metabat2_ours.155_sub o__Nanopelagicales f__Nanopelagicaceae
## Ga0485158_metabat1.076 o__CAIKKV01 f__CAITUY01
## Ga0485171_metabat1.030 o__Acetobacterales f__Acetobacteraceae
## Ga0485168_metabat2_ours.135_sub o__Phycisphaerales f__SM1A02
## Genus
## Ga0485158_metabat2_ours.098 g__CAINDZ01
## Ga0485166_metabat2_ours.038 g__CAIVAT01
## Ga0485167_metabat2_ours.023 g__CAITUY01
## Ga0485170_maxbin.090 g__UBA966
## Ga0485161_metabat2_ours.167_sub g__CAIZQW01
## Ga0485171_maxbin.130_sub g__UBA966
## Ga0485161_maxbin.110 g__
## Ga0485164_metabat2_ours.069_sub g__CAJBIL01
## Ga0485171_metabat1.063 g__
## Ga0485159_metabat2_ours.079 g__Sediminibacterium
## Ga0485171_metabat2_ours.127_sub g__Sediminibacterium
## Ga0485159_metabat2_ours.155_sub g__MAG-120802
## Ga0485158_metabat1.076 g__
## Ga0485171_metabat1.030 g__Roseomonas_B
## Ga0485168_metabat2_ours.135_sub g__UBA966
## Species
## Ga0485158_metabat2_ours.098 s__
## Ga0485166_metabat2_ours.038 s__
## Ga0485167_metabat2_ours.023 s__
## Ga0485170_maxbin.090 s__
## Ga0485161_metabat2_ours.167_sub s__
## Ga0485171_maxbin.130_sub s__
## Ga0485161_maxbin.110 s__
## Ga0485164_metabat2_ours.069_sub s__
## Ga0485171_metabat1.063 s__
## Ga0485159_metabat2_ours.079 s__Sediminibacterium sp002299885
## Ga0485171_metabat2_ours.127_sub s__Sediminibacterium sp002299885
## Ga0485159_metabat2_ours.155_sub s__
## Ga0485158_metabat1.076 s__
## Ga0485171_metabat1.030 s__
## Ga0485168_metabat2_ours.135_sub s__Analysis 6
Figure 6
library(CARlasso)
set.seed(42)
changed_col<-read.csv("metatransciptome/tax_Degree.csv")
changed_col$DO_mg_L <- ifelse(changed_col$DO_mg_L < 0, 0, changed_col$DO_mg_L)
last_10_cols <- tail(names(changed_col), 10)
changed_col <- changed_col%>%
mutate(across(all_of(last_10_cols), ~scale(., center = min(.), scale = max(.) - min(.))[,1]))
otu_res <- CARlasso(p__Krumholzibacteriota_1
+p__Bacteroidota_2
+p__Bacteroidota_3
+p__Proteobacteria_4
+p__Planctomycetota_5
+p__Bacteroidota_6
+p__Chloroflexota_7
+p__Bacteroidota_8
+p__Verrucomicrobiota_9
+p__Actinobacteriota_10
+p__Planctomycetota_11
+p__Chloroflexota_12
+p__Proteobacteria_13
+p__Firmicutes_A_14
+p__Verrucomicrobiota_15~depth+wtemp_in_celsius+specific_conductivity+chlorophyll_RFU+phycocyanin_RFU+Dissolved_organic_matter_RFU+Turbidity_RFU+DO_mg_L+PH, data = changed_col, adaptive = TRUE, link="log", n_iter = 5000, n_burn_in = 1000, thin_by = 10)## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## doneotu_res <- horseshoe(otu_res)
genomes_list <- c(
"Ga0485158_metabat2_ours.098",
"Ga0485159_metabat2_ours.079",
"Ga0485171_metabat2_ours.127_sub",
"Ga0485171_metabat2_ours.004",
"Ga0485170_maxbin.090",
"Ga0485171_metabat1.063",
"Ga0485157_metabat2_ours.019",
"Ga0485166_metabat2_ours.038",
"Ga0485167_metabat2_ours.023",
"Ga0485170_maxbin.059_sub",
"Ga0485171_maxbin.130_sub",
"Ga0485160_metabat2_ours.158_sub",
"Ga0485161_maxbin.110",
"Ga0485161_metabat1.096",
"Ga0485161_metabat2_ours.167_sub"
)
# Create a list of maps for each taxonomy level
genome_to_domain_map <- setNames(taxonomy_df$Domain, taxonomy_df$Genome)
genome_to_phylum_map <- setNames(taxonomy_df$Phylum, taxonomy_df$Genome)
genome_to_class_map <- setNames(taxonomy_df$Class, taxonomy_df$Genome)
genome_to_order_map <- setNames(taxonomy_df$Order, taxonomy_df$Genome)
genome_to_family_map <- setNames(taxonomy_df$Family, taxonomy_df$Genome)
genome_to_genus_map <- setNames(taxonomy_df$Genus, taxonomy_df$Genome)
genome_to_species_map <- setNames(taxonomy_df$Species, taxonomy_df$Genome)
# Extract taxonomy for each genome in your list
domain_names <- sapply(genomes_list, function(genome) genome_to_domain_map[[genome]])
phylum_names <- sapply(genomes_list, function(genome) genome_to_phylum_map[[genome]])
class_names <- sapply(genomes_list, function(genome) genome_to_class_map[[genome]])
order_names <- sapply(genomes_list, function(genome) genome_to_order_map[[genome]])
family_names <- sapply(genomes_list, function(genome) genome_to_family_map[[genome]])
genus_names <- sapply(genomes_list, function(genome) genome_to_genus_map[[genome]])
species_names <- sapply(genomes_list, function(genome) genome_to_species_map[[genome]])
new_column_names <- paste(phylum_names, seq_along(genomes_list), sep="_")
# Create the reference table with all levels
reference_table <- data.frame(
Original_Genome = genomes_list,
Node_Name = new_column_names,
Domain = domain_names,
Phylum = phylum_names,
Class = class_names,
Order = order_names,
Family = family_names,
Genus = genus_names,
Species = species_names
)
plot(otu_res)
Table 6
print(reference_table)## Original_Genome
## Ga0485158_metabat2_ours.098 Ga0485158_metabat2_ours.098
## Ga0485159_metabat2_ours.079 Ga0485159_metabat2_ours.079
## Ga0485171_metabat2_ours.127_sub Ga0485171_metabat2_ours.127_sub
## Ga0485171_metabat2_ours.004 Ga0485171_metabat2_ours.004
## Ga0485170_maxbin.090 Ga0485170_maxbin.090
## Ga0485171_metabat1.063 Ga0485171_metabat1.063
## Ga0485157_metabat2_ours.019 Ga0485157_metabat2_ours.019
## Ga0485166_metabat2_ours.038 Ga0485166_metabat2_ours.038
## Ga0485167_metabat2_ours.023 Ga0485167_metabat2_ours.023
## Ga0485170_maxbin.059_sub Ga0485170_maxbin.059_sub
## Ga0485171_maxbin.130_sub Ga0485171_maxbin.130_sub
## Ga0485160_metabat2_ours.158_sub Ga0485160_metabat2_ours.158_sub
## Ga0485161_maxbin.110 Ga0485161_maxbin.110
## Ga0485161_metabat1.096 Ga0485161_metabat1.096
## Ga0485161_metabat2_ours.167_sub Ga0485161_metabat2_ours.167_sub
## Node_Name Domain
## Ga0485158_metabat2_ours.098 p__Krumholzibacteriota_1 d__Bacteria
## Ga0485159_metabat2_ours.079 p__Bacteroidota_2 d__Bacteria
## Ga0485171_metabat2_ours.127_sub p__Bacteroidota_3 d__Bacteria
## Ga0485171_metabat2_ours.004 p__Proteobacteria_4 d__Bacteria
## Ga0485170_maxbin.090 p__Planctomycetota_5 d__Bacteria
## Ga0485171_metabat1.063 p__Bacteroidota_6 d__Bacteria
## Ga0485157_metabat2_ours.019 p__Chloroflexota_7 d__Bacteria
## Ga0485166_metabat2_ours.038 p__Bacteroidota_8 d__Bacteria
## Ga0485167_metabat2_ours.023 p__Verrucomicrobiota_9 d__Bacteria
## Ga0485170_maxbin.059_sub p__Actinobacteriota_10 d__Bacteria
## Ga0485171_maxbin.130_sub p__Planctomycetota_11 d__Bacteria
## Ga0485160_metabat2_ours.158_sub p__Chloroflexota_12 d__Bacteria
## Ga0485161_maxbin.110 p__Proteobacteria_13 d__Bacteria
## Ga0485161_metabat1.096 p__Firmicutes_A_14 d__Bacteria
## Ga0485161_metabat2_ours.167_sub p__Verrucomicrobiota_15 d__Bacteria
## Phylum Class
## Ga0485158_metabat2_ours.098 p__Krumholzibacteriota c__Krumholzibacteria
## Ga0485159_metabat2_ours.079 p__Bacteroidota c__Bacteroidia
## Ga0485171_metabat2_ours.127_sub p__Bacteroidota c__Bacteroidia
## Ga0485171_metabat2_ours.004 p__Proteobacteria c__Gammaproteobacteria
## Ga0485170_maxbin.090 p__Planctomycetota c__Phycisphaerae
## Ga0485171_metabat1.063 p__Bacteroidota c__Bacteroidia
## Ga0485157_metabat2_ours.019 p__Chloroflexota c__Ellin6529
## Ga0485166_metabat2_ours.038 p__Bacteroidota c__Bacteroidia
## Ga0485167_metabat2_ours.023 p__Verrucomicrobiota c__Kiritimatiellae
## Ga0485170_maxbin.059_sub p__Actinobacteriota c__Acidimicrobiia
## Ga0485171_maxbin.130_sub p__Planctomycetota c__Phycisphaerae
## Ga0485160_metabat2_ours.158_sub p__Chloroflexota c__Ellin6529
## Ga0485161_maxbin.110 p__Proteobacteria c__Gammaproteobacteria
## Ga0485161_metabat1.096 p__Firmicutes_A c__Clostridia
## Ga0485161_metabat2_ours.167_sub p__Verrucomicrobiota c__Kiritimatiellae
## Order Family
## Ga0485158_metabat2_ours.098 o__LZORAL124-64-63 f__LZORAL124-64-63
## Ga0485159_metabat2_ours.079 o__Chitinophagales f__Chitinophagaceae
## Ga0485171_metabat2_ours.127_sub o__Chitinophagales f__Chitinophagaceae
## Ga0485171_metabat2_ours.004 o__Burkholderiales f__Burkholderiaceae
## Ga0485170_maxbin.090 o__Phycisphaerales f__SM1A02
## Ga0485171_metabat1.063 o__Chitinophagales f__LD1
## Ga0485157_metabat2_ours.019 o__CSP1-4 f__UBA10416
## Ga0485166_metabat2_ours.038 o__Bacteroidales f__FEN-979
## Ga0485167_metabat2_ours.023 o__CAIKKV01 f__CAITUY01
## Ga0485170_maxbin.059_sub o__Acidimicrobiales f__Ilumatobacteraceae
## Ga0485171_maxbin.130_sub o__Phycisphaerales f__SM1A02
## Ga0485160_metabat2_ours.158_sub o__CSP1-4 f__UBA10416
## Ga0485161_maxbin.110 o__Burkholderiales f__Rhodocyclaceae
## Ga0485161_metabat1.096 o__Saccharofermentanales f__UBA5734
## Ga0485161_metabat2_ours.167_sub o__RFP12 f__UBA1067
## Genus
## Ga0485158_metabat2_ours.098 g__CAINDZ01
## Ga0485159_metabat2_ours.079 g__Sediminibacterium
## Ga0485171_metabat2_ours.127_sub g__Sediminibacterium
## Ga0485171_metabat2_ours.004 g__Polynucleobacter
## Ga0485170_maxbin.090 g__UBA966
## Ga0485171_metabat1.063 g__
## Ga0485157_metabat2_ours.019 g__UBA10416
## Ga0485166_metabat2_ours.038 g__CAIVAT01
## Ga0485167_metabat2_ours.023 g__CAITUY01
## Ga0485170_maxbin.059_sub g__UBA2093
## Ga0485171_maxbin.130_sub g__UBA966
## Ga0485160_metabat2_ours.158_sub g__UBA10416
## Ga0485161_maxbin.110 g__
## Ga0485161_metabat1.096 g__CAILLO01
## Ga0485161_metabat2_ours.167_sub g__CAIZQW01
## Species
## Ga0485158_metabat2_ours.098 s__
## Ga0485159_metabat2_ours.079 s__Sediminibacterium sp002299885
## Ga0485171_metabat2_ours.127_sub s__Sediminibacterium sp002299885
## Ga0485171_metabat2_ours.004 s__Polynucleobacter sp002292975
## Ga0485170_maxbin.090 s__
## Ga0485171_metabat1.063 s__
## Ga0485157_metabat2_ours.019 s__
## Ga0485166_metabat2_ours.038 s__
## Ga0485167_metabat2_ours.023 s__
## Ga0485170_maxbin.059_sub s__
## Ga0485171_maxbin.130_sub s__
## Ga0485160_metabat2_ours.158_sub s__
## Ga0485161_maxbin.110 s__
## Ga0485161_metabat1.096 s__
## Ga0485161_metabat2_ours.167_sub s__Analysis 8(Starting with Permutation)
In Analysis 8, we delve into the structural significance of the OTU p__Bacteroidota_3 (Ga0485162_maxbin.089) within our microbial network. Recognized for its pivotal role, this genome will undergo a permutation analysis alongside nine other randomly selected OTUs. By systematically substituting this key node with other OTUs and observing the resultant changes in network dynamics, we aim to quantify the impact of p__Bacteroidota_3 on the network’s integrity and functionality.
if (!requireNamespace("readr", quietly = TRUE)) install.packages("readr")
if (!requireNamespace("dplyr", quietly = TRUE)) install.packages("dplyr")
library(readr)## Warning: package 'readr' was built under R version 4.3.1library(dplyr)
taxonomy_file_path <- 'Fw_ lake mendota data/MAG_taxonomy.tsv'
taxonomy_df <- read_tsv(taxonomy_file_path)## Rows: 431 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (8): Genome, Domain, Phylum, Class, Order, Family, Genus, Species
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.t_count_file_path <- 'metatransciptome_count.csv'
t_count_df <- read_csv(t_count_file_path)## New names:
## Rows: 16 Columns: 442
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (1): ...1 dbl (441): Ga0485165_metabat1.097_sub, Ga0485163_maxbin.015_sub,
## Ga0485161_m...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`# Create a mapping of genomes to phylum
genome_to_phylum_map <- setNames(taxonomy_df$Phylum, taxonomy_df$Genome)
# Define your specific genomes list
genomes_list <- c(
"Ga0485159_maxbin.004_sub", "Ga0485169_maxbin.078", 'Ga0485162_maxbin.089',
"Ga0485162_metabat2_ours.126", "Ga0485159_metabat2_ours.129_sub",
"Ga0485163_metabat2_ours.245_sub", "Ga0485162_metabat2_ours.040",
"Ga0485160_metabat2_ours.184_sub", "Ga0485172_metabat2_ours.152",
"Ga0485161_metabat1.096"
)
new_column_names_list <- vector("list", length(genomes_list))
names(new_column_names_list) <- genomes_list
# Reset sequence number in case it's being reused
seq_num <- 1
for (i in seq_along(genomes_list)) {
genome <- genomes_list[i]
phylum <- genome_to_phylum_map[genome]
# Specific assignment for 'Ga0485162_maxbin.089' to ensure it gets _3
if (genome == 'Ga0485162_maxbin.089') {
assign_num <- 3
} else {
# Skip over 3 if it's already been assigned
if (seq_num == 3) {
seq_num <- 4
}
assign_num <- seq_num
seq_num <- seq_num + 1
}
if (!is.null(phylum) && genome %in% colnames(t_count_df)) {
new_column_name <- paste(phylum, assign_num, sep = "_") # Append underscore and sequence number
# Append the new column name to the list in order
new_column_names_list[[genome]] <- new_column_name
# Rename the column in t_count_df
if (genome %in% colnames(t_count_df)) {
colnames(t_count_df)[colnames(t_count_df) == genome] <- new_column_name
}
}
}
# Remove NULL entries if any genome was not found in t_count_df
new_column_names_list <- new_column_names_list[!sapply(new_column_names_list, is.null)]
# Display the first few rows of the modified DataFrame to verify changes
#print(head(t_count_df))Figure 8
library(CARlasso)
set.seed(42)
t_count_df$DO_mg_L <- ifelse(t_count_df$DO_mg_L < 0, 0, t_count_df$DO_mg_L)
last_10_cols <- tail(names(t_count_df), 10)
t_count_df <- t_count_df%>%
mutate(across(all_of(last_10_cols), ~scale(., center = min(.), scale = max(.) - min(.))[,1]))
new_column_names_list <- as.character(new_column_names_list)
sequence_numbers <- sapply(strsplit(new_column_names_list, "_"), function(x) as.numeric(tail(x, 1)))
# Create a named vector with names as original column names and values as extracted sequence numbers
named_sequences <- setNames(sequence_numbers, new_column_names_list)
# Sort the named vector by sequence numbers to get the names (original column names) in the correct order
ordered_names <- names(sort(named_sequences))
# Construct the predictor part of the formula with column names in the correct order based on sequence numbers
predictor_formula_part <- paste(ordered_names, collapse = " + ")
# Construct the full formula by appending the response variables and predictors
full_formula <- as.formula(paste(predictor_formula_part,"~", "depth + wtemp_in_celsius + specific_conductivity + chlorophyll_RFU + phycocyanin_RFU + Dissolved_organic_matter_RFU + Turbidity_RFU + DO_mg_L + PH "))
otu_res <- CARlasso(full_formula, data = t_count_df, adaptive = TRUE, link="log", n_iter = 5000, n_burn_in = 1000, thin_by = 10)## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## doneotu_res <- horseshoe(otu_res)
plot(otu_res)
reference_table <- data.frame(
Original_Genome = genomes_list,
Phylum_Levels= ordered_names
)Table 8
print(reference_table)## Original_Genome Phylum_Levels
## 1 Ga0485159_maxbin.004_sub p__Cyanobacteria_1
## 2 Ga0485169_maxbin.078 p__Proteobacteria_2
## 3 Ga0485162_maxbin.089 p__Bacteroidota_3
## 4 Ga0485162_metabat2_ours.126 p__Proteobacteria_4
## 5 Ga0485159_metabat2_ours.129_sub p__Cyanobacteria_5
## 6 Ga0485163_metabat2_ours.245_sub p__Planctomycetota_6
## 7 Ga0485162_metabat2_ours.040 p__Proteobacteria_7
## 8 Ga0485160_metabat2_ours.184_sub p__Cyanobacteria_8
## 9 Ga0485172_metabat2_ours.152 p__Actinobacteriota_9
## 10 Ga0485161_metabat1.096 p__Firmicutes_A_10Interpretation
p__Bacteroidota_3 is no longer strongly connecting to DOM. # Analysis 9 In this analysis, we concentrate on the OTU p__Verrucomicrobiota_7(Ga0485165_metabat2_ours.012_sub), distinguished for its significant connections with environmental features as revealed in Analysis 2. Recognizing its critical interactions, we opt to retain this genome while permuting it alongside 14 other randomly selected OTUs within our microbial network.
library(readr)
data <- read_csv("Fw_ lake mendota data/coverm_431_MAGS_metagenomes_reads_count.csv")## Rows: 431 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Genome
## dbl (16): 2020-10-19_23.5m, 2020-10-08_15m, 2020-10-19_15m, 2020-09-11_15m, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.data_t <- t(data)
data_df <- as.data.frame(data_t)
colnames(data_df) <- data_df[1, ]
data_df <- data_df[-1, ]
excluded_columns <- c('Ga0485157_metabat1.059',
'Ga0485167_maxbin.109',
'Ga0485162_maxbin.089',
'Ga0485161_maxbin.110',
'Ga0485161_maxbin.075',
'Ga0485157_metabat1.036',
'Ga0485165_metabat2_ours.012_sub',
'Ga0485169_maxbin.201_sub',
'Ga0485172_maxbin.081_sub',
'Ga0485168_maxbin.153',
'Ga0485172_metabat2_ours.083',
'Ga0485162_maxbin.023',
'Ga0485160_maxbin.092',
'Ga0485158_metabat2_jgi.024',
'Ga0485162_metabat1.001')
set.seed(42)
available_columns <- setdiff(colnames(data_df), excluded_columns)
random_column_names <- sample(available_columns, 14)
#print(random_column_names)
taxonomy_file_path <- 'Fw_ lake mendota data/MAG_taxonomy.tsv'
taxonomy_df <- read_tsv(taxonomy_file_path)## Rows: 431 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (8): Genome, Domain, Phylum, Class, Order, Family, Genus, Species
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.t_count_file_path <- 'metatransciptome_count.csv'
t_count_df <- read_csv(t_count_file_path)## New names:
## Rows: 16 Columns: 442
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (1): ...1 dbl (441): Ga0485165_metabat1.097_sub, Ga0485163_maxbin.015_sub,
## Ga0485161_m...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`# Create a mapping of genomes to phylum
genome_to_phylum_map <- setNames(taxonomy_df$Phylum, taxonomy_df$Genome)
# Define your specific genomes list
genomes_list <- c(
"Ga0485159_maxbin.015","Ga0485169_maxbin.202","Ga0485163_maxbin.015_sub","Ga0485159_metabat2_ours.130_sub", "Ga0485164_metabat2_jgi.001", "Ga0485162_metabat2_ours.100_sub","Ga0485165_metabat2_ours.012_sub","Ga0485161_maxbin.064","Ga0485172_metabat2_ours.152" ,"Ga0485161_metabat2_jgi.003_sub", "Ga0485168_metabat2_ours.036"
,"Ga0485157_metabat2_ours.077", "Ga0485169_metabat1.104","Ga0485169_metabat2_ours.239", "Ga0485160_metabat1.067"
)
new_column_names_list <- vector("list", length(genomes_list))
names(new_column_names_list) <- genomes_list
seq_num <- 1
for (genome in genomes_list) {
phylum <- genome_to_phylum_map[genome]
if (!is.null(phylum) && genome %in% colnames(t_count_df)) {
# Construct the new column name using the phylum and the current sequence number
new_column_name <- paste(phylum, seq_num, sep = "_")
# Update the list with the new column name
new_column_names_list[genome] <- new_column_name
# Rename the column in t_count_df
colnames(t_count_df)[colnames(t_count_df) == genome] <- new_column_name
# Increment the sequence number for the next iteration
seq_num <- seq_num + 1
}
}
# Display the first few rows of the modified DataFrame to verify changes
#print(head(t_count_df))
#print(new_column_names_list)Figure 9
library(CARlasso)
set.seed(42)
t_count_df$DO_mg_L <- ifelse(t_count_df$DO_mg_L < 0, 0, t_count_df$DO_mg_L)
last_10_cols <- tail(names(t_count_df), 10)
t_count_df <- t_count_df%>%
mutate(across(all_of(last_10_cols), ~scale(., center = min(.), scale = max(.) - min(.))[,1]))
predictor_formula_part <- paste(new_column_names_list, collapse = " + ")
# Construct the full formula by appending the response variables and predictors
full_formula <- as.formula(paste(predictor_formula_part,"~", "depth + wtemp_in_celsius + specific_conductivity + chlorophyll_RFU + phycocyanin_RFU + Dissolved_organic_matter_RFU + Turbidity_RFU +DO_mg_L + PH "))
otu_res <- CARlasso(full_formula, data = t_count_df, adaptive = TRUE, link="log", n_iter = 5000, n_burn_in = 1000, thin_by = 10)## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done otu_res <- horseshoe(otu_res)
plot(otu_res)
phylum_levels_vector <- unlist(new_column_names_list)
# Create the reference table with genomes_list and the flattened phylum_levels_vector
reference_table <- data.frame(
Original_Genome = names(phylum_levels_vector),
Phylum_Levels = phylum_levels_vector,
stringsAsFactors = FALSE, # Ensuring character data does not convert to factors
row.names = NULL
)Table 9
print(reference_table)## Original_Genome Phylum_Levels
## 1 Ga0485159_maxbin.015 p__Bacteroidota_1
## 2 Ga0485169_maxbin.202 p__Bacteroidota_2
## 3 Ga0485163_maxbin.015_sub p__Actinobacteriota_3
## 4 Ga0485159_metabat2_ours.130_sub p__Bacteroidota_4
## 5 Ga0485164_metabat2_jgi.001 p__Proteobacteria_5
## 6 Ga0485162_metabat2_ours.100_sub p__Proteobacteria_6
## 7 Ga0485165_metabat2_ours.012_sub p__Verrucomicrobiota_7
## 8 Ga0485161_maxbin.064 p__Actinobacteriota_8
## 9 Ga0485172_metabat2_ours.152 p__Actinobacteriota_9
## 10 Ga0485161_metabat2_jgi.003_sub p__Verrucomicrobiota_10
## 11 Ga0485168_metabat2_ours.036 p__Actinobacteriota_11
## 12 Ga0485157_metabat2_ours.077 p__Campylobacterota_12
## 13 Ga0485169_metabat1.104 p__Myxococcota_13
## 14 Ga0485169_metabat2_ours.239 p__Bacteroidota_14
## 15 Ga0485160_metabat1.067 p__Bdellovibrionota_15Interpretation
p__Verrucomicrobiota_7 is STILL Strongly connecting to PH and DOM, but no longer connecting to P__Proteobacteria 11.
Analysis 10
In this study, we turn our attention to the genome p__Bacteroidota_7(Ga0485160_metabat2_ours.051), a key player within our microbial network as identified by its closeness centrality measures. This genome stands out due to its pivotal connections across the network.
data <- read_csv("Fw_ lake mendota data/coverm_431_MAGS_metagenomes_reads_count.csv")## Rows: 431 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Genome
## dbl (16): 2020-10-19_23.5m, 2020-10-08_15m, 2020-10-19_15m, 2020-09-11_15m, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.data_t <- t(data)
data_df <- as.data.frame(data_t)
colnames(data_df) <- data_df[1, ]
data_df <- data_df[-1, ]
excluded_columns <- c("Ga0485157_metabat1.069",
"Ga0485157_metabat2_ours.025",
"Ga0485157_metabat2_ours.091",
"Ga0485158_metabat2_ours.184",
"Ga0485159_maxbin.015",
"Ga0485160_maxbin.155_sub",
"Ga0485160_metabat2_ours.051",
"Ga0485160_metabat2_ours.071",
"Ga0485161_metabat1.109_sub",
"Ga0485162_metabat2_ours.036",
"Ga0485163_maxbin.002_sub",
"Ga0485163_maxbin.104_sub",
"Ga0485163_maxbin.129",
"Ga0485163_maxbin.130_sub",
"Ga0485163_maxbin.183_sub")
set.seed(42)
available_columns <- setdiff(colnames(data_df), excluded_columns)
random_column_names <- sample(available_columns, 14)
taxonomy_file_path <- 'Fw_ lake mendota data/MAG_taxonomy.tsv'
taxonomy_df <- read_tsv(taxonomy_file_path)## Rows: 431 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (8): Genome, Domain, Phylum, Class, Order, Family, Genus, Species
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.t_count_file_path <- 'metatransciptome_count.csv'
t_count_df <- read_csv(t_count_file_path)## New names:
## Rows: 16 Columns: 442
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (1): ...1 dbl (441): Ga0485165_metabat1.097_sub, Ga0485163_maxbin.015_sub,
## Ga0485161_m...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`# Create a mapping of genomes to phylum
genome_to_phylum_map <- setNames(taxonomy_df$Phylum, taxonomy_df$Genome)
# Define your specific genomes list
genomes_list <- c(
"Ga0485159_maxbin.027","Ga0485169_maxbin.226_sub","Ga0485163_maxbin.041_sub","Ga0485159_metabat2_ours.135_sub","Ga0485165_metabat1.064_sub","Ga0485162_metabat2_ours.116","Ga0485160_metabat2_ours.051","Ga0485161_metabat1.011","Ga0485172_metabat2_ours.152","Ga0485161_metabat2_ours.019_sub","Ga0485168_metabat2_ours.050_sub","Ga0485157_metabat2_ours.077","Ga0485169_metabat1.187_sub","Ga0485169_metabat2_ours.242", "Ga0485160_metabat1.086"
)
new_column_names_list <- vector("list", length(genomes_list))
names(new_column_names_list) <- genomes_list
seq_num <- 1
for (genome in genomes_list) {
phylum <- genome_to_phylum_map[genome]
if (!is.null(phylum) && genome %in% colnames(t_count_df)) {
# Construct the new column name using the phylum and the current sequence number
new_column_name <- paste(phylum, seq_num, sep = "_")
# Update the list with the new column name
new_column_names_list[genome] <- new_column_name
# Rename the column in t_count_df
colnames(t_count_df)[colnames(t_count_df) == genome] <- new_column_name
# Increment the sequence number for the next iteration
seq_num <- seq_num + 1
}
}
# Display the first few rows of the modified DataFrame to verify changes
#print(head(t_count_df))
#print(new_column_names_list)Figure 10
library(CARlasso)
set.seed(42)
t_count_df$DO_mg_L <- ifelse(t_count_df$DO_mg_L < 0, 0, t_count_df$DO_mg_L)
last_10_cols <- tail(names(t_count_df), 10)
t_count_df <- t_count_df%>%
mutate(across(all_of(last_10_cols), ~scale(., center = min(.), scale = max(.) - min(.))[,1]))
predictor_formula_part <- paste(new_column_names_list, collapse = " + ")
# Construct the full formula by appending the response variables and predictors
full_formula <- as.formula(paste(predictor_formula_part,"~", "depth + wtemp_in_celsius + specific_conductivity + chlorophyll_RFU + phycocyanin_RFU + Dissolved_organic_matter_RFU + Turbidity_RFU + DO_mg_L + PH "))
otu_res <- CARlasso(full_formula, data = t_count_df, adaptive = TRUE, link="log", n_iter = 5000, n_burn_in = 1000, thin_by = 10)## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## doneotu_res <- horseshoe(otu_res)
plot(otu_res)
phylum_levels_vector <- unlist(new_column_names_list)
# Create the reference table with genomes_list and the flattened phylum_levels_vector
reference_table <- data.frame(
Original_Genome = names(phylum_levels_vector),
Phylum_Levels = phylum_levels_vector,
stringsAsFactors = FALSE, # Ensuring character data does not convert to factors
row.names = NULL
)Table 10
print(reference_table)## Original_Genome Phylum_Levels
## 1 Ga0485159_maxbin.027 p__Cyanobacteria_1
## 2 Ga0485169_maxbin.226_sub p__Verrucomicrobiota_2
## 3 Ga0485163_maxbin.041_sub p__Proteobacteria_3
## 4 Ga0485159_metabat2_ours.135_sub p__Planctomycetota_4
## 5 Ga0485165_metabat1.064_sub p__Bacteroidota_5
## 6 Ga0485162_metabat2_ours.116 p__Verrucomicrobiota_6
## 7 Ga0485160_metabat2_ours.051 p__Bacteroidota_7
## 8 Ga0485161_metabat1.011 p__Hydrogenedentota_8
## 9 Ga0485172_metabat2_ours.152 p__Actinobacteriota_9
## 10 Ga0485161_metabat2_ours.019_sub p__Verrucomicrobiota_10
## 11 Ga0485168_metabat2_ours.050_sub p__Actinobacteriota_11
## 12 Ga0485157_metabat2_ours.077 p__Campylobacterota_12
## 13 Ga0485169_metabat1.187_sub p__Verrucomicrobiota_13
## 14 Ga0485169_metabat2_ours.242 p__Verrucomicrobiota_14
## 15 Ga0485160_metabat1.086 p__Cyanobacteria_15Interpretation
p__Bacteroidota_7 is not connecting to p__Actinobacteriota_2 and p__Bacteroidota_14. However, pior to permutation, the MAGs are more correlated with each other comparing with the environmental features, and this property still remains after permutation.
Analysis 11
This analysis focuses on the genome p__Verrucomicrobiota_9(Ga0485167_metabat2_ours.023), a significant entity within our microbial network due to its degree centrality. Highlighting its extensive connections within the network, we undertake a permutation analysis where p__Verrucomicrobiota_9 is swapped with 14 other randomly selected OTUs. This process aims to examine the impact of p__Verrucomicrobiota_9 on the network’s structure and functionality by observing how the network adapts to the reassignment of this highly connected genome.
# Proceed with the rest of the script
data <- read_csv("Fw_ lake mendota data/coverm_431_MAGS_metagenomes_reads_count.csv")## Rows: 431 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Genome
## dbl (16): 2020-10-19_23.5m, 2020-10-08_15m, 2020-10-19_15m, 2020-09-11_15m, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.data_t <- t(data)
data_df <- as.data.frame(data_t)
colnames(data_df) <- data_df[1, ]
data_df <- data_df[-1, ]
excluded_columns <- c("Ga0485158_metabat2_ours.098",
"Ga0485159_metabat2_ours.079",
"Ga0485171_metabat2_ours.127_sub",
"Ga0485171_metabat2_ours.004",
"Ga0485170_maxbin.090",
"Ga0485171_metabat1.063",
"Ga0485157_metabat2_ours.019",
"Ga0485166_metabat2_ours.038",
"Ga0485167_metabat2_ours.023",
"Ga0485170_maxbin.059_sub",
"Ga0485171_maxbin.130_sub",
"Ga0485160_metabat2_ours.158_sub",
"Ga0485161_maxbin.110",
"Ga0485161_metabat1.096",
"Ga0485161_metabat2_ours.167_sub")
set.seed(42)
available_columns <- setdiff(colnames(data_df), excluded_columns)
random_column_names <- sample(available_columns, 14)
#print(random_column_names)
taxonomy_file_path <- 'Fw_ lake mendota data/MAG_taxonomy.tsv'
taxonomy_df <- read_tsv(taxonomy_file_path)## Rows: 431 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (8): Genome, Domain, Phylum, Class, Order, Family, Genus, Species
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.t_count_file_path <- 'metatransciptome_count.csv'
t_count_df <- read_csv(t_count_file_path)## New names:
## Rows: 16 Columns: 442
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (1): ...1 dbl (441): Ga0485165_metabat1.097_sub, Ga0485163_maxbin.015_sub,
## Ga0485161_m...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`# Create a mapping of genomes to phylum
genome_to_phylum_map <- setNames(taxonomy_df$Phylum, taxonomy_df$Genome)
genomes_list <- c(
"Ga0485159_maxbin.004_sub","Ga0485169_maxbin.153","Ga0485163_maxbin.002_sub","Ga0485159_metabat2_ours.130_sub","Ga0485164_metabat1.042_sub","Ga0485162_metabat2_ours.088" ,"Ga0485161_maxbin.064","Ga0485172_metabat2_ours.152","Ga0485167_metabat2_ours.023" ,"Ga0485161_metabat2_jgi.003_sub","Ga0485168_metabat2_ours.010","Ga0485157_metabat2_ours.068_sub","Ga0485169_maxbin.226_sub","Ga0485169_metabat2_ours.154","Ga0485160_maxbin.164_sub"
)
new_column_names_list <- vector("list", length(genomes_list))
names(new_column_names_list) <- genomes_list
seq_num <- 1
for (genome in genomes_list) {
phylum <- genome_to_phylum_map[genome]
if (!is.null(phylum) && genome %in% colnames(t_count_df)) {
# Construct the new column name using the phylum and the current sequence number
new_column_name <- paste(phylum, seq_num, sep = "_")
# Update the list with the new column name
new_column_names_list[genome] <- new_column_name
# Rename the column in t_count_df
colnames(t_count_df)[colnames(t_count_df) == genome] <- new_column_name
# Increment the sequence number for the next iteration
seq_num <- seq_num + 1
}
}
# Display the first few rows of the modified DataFrame to verify changes
#print(head(t_count_df))
#print(new_column_names_list)Figure 11
library(CARlasso)
set.seed(42)
t_count_df$DO_mg_L <- ifelse(t_count_df$DO_mg_L < 0, 0, t_count_df$DO_mg_L)
last_10_cols <- tail(names(t_count_df), 10)
t_count_df <- t_count_df%>%
mutate(across(all_of(last_10_cols), ~scale(., center = min(.), scale = max(.) - min(.))[,1]))
predictor_formula_part <- paste(new_column_names_list, collapse = " + ")
# Construct the full formula by appending the response variables and predictors
full_formula <- as.formula(paste(predictor_formula_part,"~", "depth + wtemp_in_celsius + specific_conductivity + chlorophyll_RFU + phycocyanin_RFU + Dissolved_organic_matter_RFU + Turbidity_RFU + DO_sat_perc + DO_mg_L + PH "))
otu_res <- CARlasso(full_formula, data = t_count_df, adaptive = TRUE, link="log", n_iter = 5000, n_burn_in = 1000, thin_by = 10)## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done otu_res <- horseshoe(otu_res)
plot(otu_res)
phylum_levels_vector <- unlist(new_column_names_list)
# Create the reference table with genomes_list and the flattened phylum_levels_vector
reference_table <- data.frame(
Original_Genome = names(phylum_levels_vector),
Phylum_Levels = phylum_levels_vector,
stringsAsFactors = FALSE, # Ensuring character data does not convert to factors
row.names = NULL
)Table 11
print(reference_table)## Original_Genome Phylum_Levels
## 1 Ga0485159_maxbin.004_sub p__Cyanobacteria_1
## 2 Ga0485169_maxbin.153 p__Proteobacteria_2
## 3 Ga0485163_maxbin.002_sub p__Planctomycetota_3
## 4 Ga0485159_metabat2_ours.130_sub p__Bacteroidota_4
## 5 Ga0485164_metabat1.042_sub p__Proteobacteria_5
## 6 Ga0485162_metabat2_ours.088 p__Actinobacteriota_6
## 7 Ga0485161_maxbin.064 p__Actinobacteriota_7
## 8 Ga0485172_metabat2_ours.152 p__Actinobacteriota_8
## 9 Ga0485167_metabat2_ours.023 p__Verrucomicrobiota_9
## 10 Ga0485161_metabat2_jgi.003_sub p__Verrucomicrobiota_10
## 11 Ga0485168_metabat2_ours.010 p__Proteobacteria_11
## 12 Ga0485157_metabat2_ours.068_sub p__Bacteroidota_12
## 13 Ga0485169_maxbin.226_sub p__Verrucomicrobiota_13
## 14 Ga0485169_metabat2_ours.154 p__Firmicutes_A_14
## 15 Ga0485160_maxbin.164_sub p__Bacteroidota_15Interpretation
After permuting genome p__Verrucomicrobiota_9(Ga0485167_metabat2_ours.023) with 14 other random OTUs, we note a significant change: its previously strong connections with various environmental features(Dissolved organic matter and PH), observed in Analysis 6, have dissipated. This shift highlights the community-dependent nature of p__Bacteroidota_6’s role and underscores the dynamic adaptability of microbial networks to changes in their composition.
Analysis 12
In this expanded analysis, we continue to focus on genome p__Verrucomicrobiota_9(Ga0485167_metabat2_ours.023), recognized for its substantial network connections. This iteration involves permuting p__Verrucomicrobiota_9 alongside 14 other randomly selected OTUs across 100 random network configurations. The objective is to quantitatively assess the significance of p__Verrucomicrobiota_9 within these varied network structures by measuring how often, within these 100 permutations, the genome emerges as important. Specifically, this OTU is deemed important if its edge value falls above the 90th percentile in the distribution of all edge values within the network. This systematic repetition provides a robust statistical framework to evaluate the resilience of p__Verrucomicrobiota_9’s network influence and its ecological role across multiple hypothetical community compositions. The outcome, reported as a percentage, will highlight the frequency at which p__Verrucomicrobiota_9 retains its central importance, offering deeper insights into its consistency as a key connector within the microbial community under diverse structural scenarios.
library(readr)
library(CARlasso)
# Assuming initial data loading and transformation is done
data <- read_csv("Fw_ lake mendota data/coverm_431_MAGS_metagenomes_reads_count.csv")## Rows: 431 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Genome
## dbl (16): 2020-10-19_23.5m, 2020-10-08_15m, 2020-10-19_15m, 2020-09-11_15m, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.data_t <- t(data)
data_df <- as.data.frame(data_t)
colnames(data_df) <- data_df[1, ]
data_df <- data_df[-1, ]
# Load the taxonomy and T-count files
taxonomy_df <- read_tsv('Fw_ lake mendota data/MAG_taxonomy.tsv')## Rows: 431 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (8): Genome, Domain, Phylum, Class, Order, Family, Genus, Species
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.t_count_df <- read_csv('metatransciptome_count.csv')## New names:
## Rows: 16 Columns: 442
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (1): ...1 dbl (441): Ga0485165_metabat1.097_sub, Ga0485163_maxbin.015_sub,
## Ga0485161_m...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`# Create a mapping of genomes to phylum
genome_to_phylum_map <- setNames(taxonomy_df$Phylum, taxonomy_df$Genome)
excluded_columns <- c(
"Ga0485158_metabat2_ours.098",
"Ga0485159_metabat2_ours.079",
"Ga0485171_metabat2_ours.127_sub",
"Ga0485171_metabat2_ours.004",
"Ga0485170_maxbin.090",
"Ga0485171_metabat1.063",
"Ga0485157_metabat2_ours.019",
"Ga0485166_metabat2_ours.038",
"Ga0485167_metabat2_ours.023",
"Ga0485170_maxbin.059_sub",
"Ga0485171_maxbin.130_sub",
"Ga0485160_metabat2_ours.158_sub",
"Ga0485161_maxbin.110",
"Ga0485161_metabat1.096",
"Ga0485161_metabat2_ours.167_sub",
'Ga0485157_metabat2_jgi.016',
'Ga0485163_metabat1.131',
'Ga0485161_metabat2_jgi.016',
'Ga0485161_metabat2_ours.188_sub',
'Ga0485158_metabat1.076',
'Ga0485167_maxbin.132',
'Ga0485161_metabat2_jgi.003_sub',
'Ga0485167_metabat2_ours.110_sub',
'Ga0485169_metabat2_ours.047_sub',
'Ga0485167_metabat2_ours.008',
'Ga0485172_maxbin.051_sub',
'Ga0485172_maxbin.047_sub',
'Ga0485168_metabat2_ours.001_sub',
'Ga0485163_metabat2_ours.198',
'Ga0485170_metabat2_ours.102',
'Ga0485169_maxbin.156',
'Ga0485159_maxbin.127_sub',
'Ga0485159_metabat2_jgi.002',
'Ga0485160_metabat2_jgi.018',
'Ga0485166_maxbin.099',
'Ga0485163_metabat2_jgi.007',
'Ga0485160_maxbin.046',
'Ga0485163_maxbin.058_sub',
'Ga0485164_metabat1.042_sub',
'Ga0485163_maxbin.075_sub',
'Ga0485162_metabat2_ours.050'
)
available_columns <- setdiff(colnames(data_df), excluded_columns)
#set.seed(42) # For reproducibility
# Function to update t_count_df based on sampled genomes
update_t_count_df <- function(sampled_genomes, t_count_df, genome_to_phylum_map) {
updated_column_names <- vector("character", length(sampled_genomes))
for (i in seq_along(sampled_genomes)) {
genome <- sampled_genomes[i]
phylum <- genome_to_phylum_map[genome]
if (!is.na(phylum)) {
new_column_name <- paste(phylum, i, sep = "_")
updated_column_names[i] <- new_column_name
if (genome %in% colnames(t_count_df)) {
colnames(t_count_df)[colnames(t_count_df) == genome] <- new_column_name
}
} else {
updated_column_names[i] <- genome # Use original name if no mapping found
}
}
return(list(updated_df = t_count_df, selected_mags = updated_column_names))
}
results_90percent <- list()
# Initialize counters
count_values_abs_greater_than_10_in_col6 <- 0
count_values_abs_greater_than_10_in_rows_8_and_9_col6 <- 0
base_seed <- 42
for (i in 1:100) {
set.seed(base_seed + i)
sampled_columns <- sample(available_columns, 8)
additional_columns <- sample(setdiff(available_columns, sampled_columns), 6)
sampled_genomes <- c(sampled_columns, "Ga0485167_metabat2_ours.023", additional_columns)
list_results <- update_t_count_df(sampled_genomes, t_count_df, genome_to_phylum_map)
updated_t_count_df <- list_results$updated_df
new_column_names_list <- list_results$selected_mags
changed_col <- updated_t_count_df
# Replace negative DO_mg_L values with 0
changed_col$DO_mg_L <- ifelse(changed_col$DO_mg_L < 0, 0, changed_col$DO_mg_L)
# Get the names of the last 10 columns
last_10_cols <- tail(names(changed_col), 10)
# Apply min-max scaling to the last 10 columns
changed_col <- changed_col %>%
mutate(across(all_of(last_10_cols), ~ ( . - min(.)) / (max(.) - min(.)) ))
# Construct the predictor formula part with the updated column names
predictor_formula_part <- paste(new_column_names_list, collapse = " + ")
full_formula <- as.formula(paste(predictor_formula_part,"~", "depth + wtemp_in_celsius + specific_conductivity + chlorophyll_RFU + phycocyanin_RFU + Dissolved_organic_matter_RFU + Turbidity_RFU +DO_mg_L + PH "))
# Using tryCatch to handle potential errors in CARlasso
otu_res <- tryCatch({
set.seed(42)
CARlasso(full_formula, data = changed_col, adaptive = TRUE, link = "log", n_iter = 5000, n_burn_in = 1000, thin_by = 10)
}, error = function(e) {
# If an error occurs, return NULL and proceed to the next iteration
NULL
})
# Check if CARlasso ran successfully
if (is.null(otu_res)) {
# Skip the rest of this iteration if CARlasso failed
next
}
# Apply horseshoe transformation
#otu_res_hs <- horseshoe(otu_res)
# Assuming otu_res$point_est$CAR$B is your matrix
data_matrix <- otu_res$point_est$CAR$B
# Convert the matrix elements to their absolute values
data_matrix_abs <- abs(data_matrix)
# Convert the absolute value matrix to a vector
data_vector <- as.vector(data_matrix_abs)
# Calculate the 90th percentile of the absolute values
ninety_percentile <- quantile(data_vector, 0.9)
# Check and count if any absolute value in column 9
if (any(abs(otu_res$point_est$CAR$B[, 9]) > ninety_percentile)) {
count_values_abs_greater_than_10_in_col6 <- count_values_abs_greater_than_10_in_col6 + 1
}
# Check and count if absolute values in rows 8 and 9 of column 9
if (abs(otu_res$point_est$CAR$B[6, 9]) > ninety_percentile & abs(otu_res$point_est$CAR$B[9, 9]) > ninety_percentile) {
count_values_abs_greater_than_10_in_rows_8_and_9_col6 <- count_values_abs_greater_than_10_in_rows_8_and_9_col6 + 1
}
# Store the horseshoe analysis results
results_90percent[[i]] <- ninety_percentile
}## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done# Calculate percentages
percentage_col14 <- (count_values_abs_greater_than_10_in_col6 / sum(!sapply(results_90percent, is.null))) * 100
percentage_rows_8_and_9_col6 <- (count_values_abs_greater_than_10_in_rows_8_and_9_col6/ sum(!sapply(results_90percent, is.null))) * 100Value of percenatge
cat("Percentage of iterations where abs value is upon the 90 pecent quantile is:", percentage_rows_8_and_9_col6, "%\n")## Percentage of iterations where abs value is upon the 90 pecent quantile is: 6.060606 %Analysis 13
In this phase of our study, We aim to assess the significance of Genome p__Verrucomicrobiota_7(Ga0485165_metabat2_ours.012_sub) across 100 random microbial networks by permuting it with 14 other OTUs. Significance is determined if its connectivity exceeds the 90th percentile in edge values, quantifying its robustness as a key connector.
library(readr)
library(CARlasso)
# Assuming initial data loading and transformation is done
data <- read_csv("Fw_ lake mendota data/coverm_431_MAGS_metagenomes_reads_count.csv")## Rows: 431 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Genome
## dbl (16): 2020-10-19_23.5m, 2020-10-08_15m, 2020-10-19_15m, 2020-09-11_15m, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.data_t <- t(data)
data_df <- as.data.frame(data_t)
colnames(data_df) <- data_df[1, ]
data_df <- data_df[-1, ]
# Load the taxonomy and T-count files
taxonomy_df <- read_tsv('Fw_ lake mendota data/MAG_taxonomy.tsv')## Rows: 431 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (8): Genome, Domain, Phylum, Class, Order, Family, Genus, Species
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.t_count_df <- read_csv('metatransciptome_count.csv')## New names:
## Rows: 16 Columns: 442
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (1): ...1 dbl (441): Ga0485165_metabat1.097_sub, Ga0485163_maxbin.015_sub,
## Ga0485161_m...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`# Create a mapping of genomes to phylum
genome_to_phylum_map <- setNames(taxonomy_df$Phylum, taxonomy_df$Genome)
excluded_columns <- c(
'Ga0485157_metabat1.059',
'Ga0485167_maxbin.109',
'Ga0485162_maxbin.089',
'Ga0485161_maxbin.110',
'Ga0485161_maxbin.075',
'Ga0485157_metabat1.036',
'Ga0485165_metabat2_ours.012_sub',
'Ga0485169_maxbin.201_sub',
'Ga0485172_maxbin.081_sub',
'Ga0485168_maxbin.153',
'Ga0485172_metabat2_ours.083',
'Ga0485162_maxbin.023',
'Ga0485160_maxbin.092',
'Ga0485158_metabat2_jgi.024',
'Ga0485162_metabat1.001',
'Ga0485157_metabat2_jgi.016',
'Ga0485163_metabat1.131',
'Ga0485161_metabat2_jgi.016',
'Ga0485161_metabat2_ours.188_sub',
'Ga0485158_metabat1.076',
'Ga0485167_maxbin.132',
'Ga0485161_metabat2_jgi.003_sub',
'Ga0485167_metabat2_ours.110_sub',
'Ga0485169_metabat2_ours.047_sub',
'Ga0485167_metabat2_ours.008',
'Ga0485172_maxbin.051_sub',
'Ga0485172_maxbin.047_sub',
'Ga0485168_metabat2_ours.001_sub',
'Ga0485163_metabat2_ours.198',
'Ga0485170_metabat2_ours.102',
'Ga0485169_maxbin.156',
'Ga0485159_maxbin.127_sub',
'Ga0485159_metabat2_jgi.002',
'Ga0485160_metabat2_jgi.018',
'Ga0485166_maxbin.099',
'Ga0485163_metabat2_jgi.007',
'Ga0485160_maxbin.046',
'Ga0485163_maxbin.058_sub',
'Ga0485164_metabat1.042_sub',
'Ga0485163_maxbin.075_sub',
'Ga0485162_metabat2_ours.050'
)
available_columns <- setdiff(colnames(data_df), excluded_columns)
#set.seed(42) # For reproducibility
# Function to update t_count_df based on sampled genomes
update_t_count_df <- function(sampled_genomes, t_count_df, genome_to_phylum_map) {
updated_column_names <- vector("character", length(sampled_genomes))
for (i in seq_along(sampled_genomes)) {
genome <- sampled_genomes[i]
phylum <- genome_to_phylum_map[genome]
if (!is.na(phylum)) {
new_column_name <- paste(phylum, i, sep = "_")
updated_column_names[i] <- new_column_name
if (genome %in% colnames(t_count_df)) {
colnames(t_count_df)[colnames(t_count_df) == genome] <- new_column_name
}
} else {
updated_column_names[i] <- genome # Use original name if no mapping found
}
}
return(list(updated_df = t_count_df, selected_mags = updated_column_names))
}
results_90percent <- list()
# Initialize counters
count_values_abs_greater_than_10_in_col4 <- 0
count_values_abs_greater_than_10_in_rows_8_and_9_col4 <- 0
base_seed <- 42
for (i in 1:100) {
set.seed(base_seed + i)
sampled_columns <- sample(available_columns, 6)
additional_columns <- sample(setdiff(available_columns, sampled_columns), 8)
sampled_genomes <- c(sampled_columns, "Ga0485165_metabat2_ours.012_sub", additional_columns)
list_results <- update_t_count_df(sampled_genomes, t_count_df, genome_to_phylum_map)
updated_t_count_df <- list_results$updated_df
new_column_names_list <- list_results$selected_mags
changed_col <- updated_t_count_df
# Replace negative DO_mg_L values with 0
changed_col$DO_mg_L <- ifelse(changed_col$DO_mg_L < 0, 0, changed_col$DO_mg_L)
# Get the names of the last 10 columns
last_10_cols <- tail(names(changed_col), 10)
# Apply min-max scaling to the last 10 columns
changed_col <- changed_col %>%
mutate(across(all_of(last_10_cols), ~ ( . - min(.)) / (max(.) - min(.)) ))
# Construct the predictor formula part with the updated column names
predictor_formula_part <- paste(new_column_names_list, collapse = " + ")
full_formula <- as.formula(paste(predictor_formula_part,"~", "depth + wtemp_in_celsius + specific_conductivity + chlorophyll_RFU + phycocyanin_RFU + Dissolved_organic_matter_RFU + Turbidity_RFU +DO_mg_L + PH "))
# Using tryCatch to handle potential errors in CARlasso
otu_res <- tryCatch({
set.seed(42)
CARlasso(full_formula, data = changed_col, adaptive = TRUE, link = "log", n_iter = 5000, n_burn_in = 1000, thin_by = 10)
}, error = function(e) {
# If an error occurs, return NULL and proceed to the next iteration
NULL
})
# Check if CARlasso ran successfully
if (is.null(otu_res)) {
# Skip the rest of this iteration if CARlasso failed
next
}
# Apply horseshoe transformation
#otu_res_hs <- horseshoe(otu_res)
# Assuming otu_res$point_est$CAR$B is your matrix
data_matrix <- otu_res$point_est$CAR$B
# Convert the matrix elements to their absolute values
data_matrix_abs <- abs(data_matrix)
# Convert the absolute value matrix to a vector
data_vector <- as.vector(data_matrix_abs)
# Calculate the 90th percentile of the absolute values
ninety_percentile_15 <- quantile(data_vector, 0.9)
# Check and count if any absolute value in column 15
if (any(abs(otu_res$point_est$CAR$B[, 7]) > ninety_percentile_15)) {
count_values_abs_greater_than_10_in_col4 <- count_values_abs_greater_than_10_in_col4 + 1
}
# Check and count if absolute values in rows 5 and 6 of column 15
if (abs(otu_res$point_est$CAR$B[6, 7]) > ninety_percentile & abs(otu_res$point_est$CAR$B[9, 7]) > ninety_percentile){
count_values_abs_greater_than_10_in_rows_8_and_9_col4 <- count_values_abs_greater_than_10_in_rows_8_and_9_col4 + 1
}
# Store the horseshoe analysis results
results_90percent[[i]] <- ninety_percentile_15
}## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## donepercentage_col4 <- (count_values_abs_greater_than_10_in_col4 / sum(!sapply(results_90percent, is.null))) * 100
percentage_rows_8_and_9_col4 <- (count_values_abs_greater_than_10_in_rows_8_and_9_col4 / sum(!sapply(results_90percent, is.null))) * 100
# Print results
#cat("Percentage of iterations where any abs value > 10 in col 14:", percentage_col14, "%\n")
#cat("Percentage of iterations where abs value is upon the 90 pecent quantile is:", percentage_rows_8_and_9_col4, "%\n")Value of percenatge
cat("Percentage of iterations where abs value is upon the 90 pecent quantile is:", percentage_rows_8_and_9_col4, "%\n")## Percentage of iterations where abs value is upon the 90 pecent quantile is: 30.30303 %Interpretation
Analysis 14
In this phase of our study, We aim to assess the significance of Genome p__Bacteroidota_9(Ga0485171_metabat1.063) across 100 random microbial networks by permuting it with 14 other OTUs. Significance is determined if its connectivity exceeds the 90th percentile in edge values, quantifying its robustness as a key connector.(based on analysis 5)
library(readr)
library(CARlasso)
# Assuming initial data loading and transformation is done
data <- read_csv("Fw_ lake mendota data/coverm_431_MAGS_metagenomes_reads_count.csv")## Rows: 431 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Genome
## dbl (16): 2020-10-19_23.5m, 2020-10-08_15m, 2020-10-19_15m, 2020-09-11_15m, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.data_t <- t(data)
data_df <- as.data.frame(data_t)
colnames(data_df) <- data_df[1, ]
data_df <- data_df[-1, ]
# Load the taxonomy and T-count files
taxonomy_df <- read_tsv('Fw_ lake mendota data/MAG_taxonomy.tsv')## Rows: 431 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (8): Genome, Domain, Phylum, Class, Order, Family, Genus, Species
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.t_count_df <- read_csv('metatransciptome_count.csv')## New names:
## Rows: 16 Columns: 442
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (1): ...1 dbl (441): Ga0485165_metabat1.097_sub, Ga0485163_maxbin.015_sub,
## Ga0485161_m...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`# Create a mapping of genomes to phylum
genome_to_phylum_map <- setNames(taxonomy_df$Phylum, taxonomy_df$Genome)
excluded_columns <- c(
"Ga0485158_metabat2_ours.098",
"Ga0485166_metabat2_ours.038",
"Ga0485167_metabat2_ours.023",
"Ga0485170_maxbin.090",
"Ga0485161_metabat2_ours.167_sub",
"Ga0485171_maxbin.130_sub",
"Ga0485161_maxbin.110",
"Ga0485164_metabat2_ours.069_sub",
"Ga0485171_metabat1.063",
"Ga0485159_metabat2_ours.079",
"Ga0485171_metabat2_ours.127_sub",
"Ga0485159_metabat2_ours.155_sub",
"Ga0485158_metabat1.076",
"Ga0485171_metabat1.030",
"Ga0485168_metabat2_ours.135_sub",
'Ga0485157_metabat2_jgi.016',
'Ga0485163_metabat1.131',
'Ga0485161_metabat2_jgi.016',
'Ga0485161_metabat2_ours.188_sub',
'Ga0485158_metabat1.076',
'Ga0485167_maxbin.132',
'Ga0485161_metabat2_jgi.003_sub',
'Ga0485167_metabat2_ours.110_sub',
'Ga0485169_metabat2_ours.047_sub',
'Ga0485167_metabat2_ours.008',
'Ga0485172_maxbin.051_sub',
'Ga0485172_maxbin.047_sub',
'Ga0485168_metabat2_ours.001_sub',
'Ga0485163_metabat2_ours.198',
'Ga0485170_metabat2_ours.102',
'Ga0485169_maxbin.156',
'Ga0485159_maxbin.127_sub',
'Ga0485159_metabat2_jgi.002',
'Ga0485160_metabat2_jgi.018',
'Ga0485166_maxbin.099',
'Ga0485163_metabat2_jgi.007',
'Ga0485160_maxbin.046',
'Ga0485163_maxbin.058_sub',
'Ga0485164_metabat1.042_sub',
'Ga0485163_maxbin.075_sub',
'Ga0485162_metabat2_ours.050'
)
available_columns <- setdiff(colnames(data_df), excluded_columns)
#set.seed(42) # For reproducibility
# Function to update t_count_df based on sampled genomes
update_t_count_df <- function(sampled_genomes, t_count_df, genome_to_phylum_map) {
updated_column_names <- vector("character", length(sampled_genomes))
for (i in seq_along(sampled_genomes)) {
genome <- sampled_genomes[i]
phylum <- genome_to_phylum_map[genome]
if (!is.na(phylum)) {
new_column_name <- paste(phylum, i, sep = "_")
updated_column_names[i] <- new_column_name
if (genome %in% colnames(t_count_df)) {
colnames(t_count_df)[colnames(t_count_df) == genome] <- new_column_name
}
} else {
updated_column_names[i] <- genome # Use original name if no mapping found
}
}
return(list(updated_df = t_count_df, selected_mags = updated_column_names))
}
results_90percent <- list()
# Initialize counters
count_values_abs_greater_than_10_in_col4 <- 0
count_values_abs_greater_than_10_in_rows_8_and_9_col4 <- 0
base_seed <- 42
for (i in 1:100) {
set.seed(base_seed + i)
sampled_columns <- sample(available_columns, 8)
additional_columns <- sample(setdiff(available_columns, sampled_columns), 6)
sampled_genomes <- c(sampled_columns, "Ga0485171_metabat1.063", additional_columns)
list_results <- update_t_count_df(sampled_genomes, t_count_df, genome_to_phylum_map)
updated_t_count_df <- list_results$updated_df
new_column_names_list <- list_results$selected_mags
changed_col <- updated_t_count_df
# Replace negative DO_mg_L values with 0
changed_col$DO_mg_L <- ifelse(changed_col$DO_mg_L < 0, 0, changed_col$DO_mg_L)
# Get the names of the last 10 columns
last_10_cols <- tail(names(changed_col), 10)
# Apply min-max scaling to the last 10 columns
changed_col <- changed_col %>%
mutate(across(all_of(last_10_cols), ~ ( . - min(.)) / (max(.) - min(.)) ))
# Construct the predictor formula part with the updated column names
predictor_formula_part <- paste(new_column_names_list, collapse = " + ")
full_formula <- as.formula(paste(predictor_formula_part,"~", "depth + wtemp_in_celsius + specific_conductivity + chlorophyll_RFU + phycocyanin_RFU + Dissolved_organic_matter_RFU + Turbidity_RFU + DO_sat_perc + DO_mg_L + PH "))
# Using tryCatch to handle potential errors in CARlasso
otu_res <- tryCatch({
set.seed(42)
CARlasso(full_formula, data = changed_col, adaptive = TRUE, link = "log", n_iter = 5000, n_burn_in = 1000, thin_by = 10)
}, error = function(e) {
# If an error occurs, return NULL and proceed to the next iteration
NULL
})
# Check if CARlasso ran successfully
if (is.null(otu_res)) {
# Skip the rest of this iteration if CARlasso failed
next
}
# Apply horseshoe transformation
#otu_res_hs <- horseshoe(otu_res)
# Assuming otu_res$point_est$CAR$B is your matrix
data_matrix <- otu_res$point_est$CAR$B
# Convert the matrix elements to their absolute values
data_matrix_abs <- abs(data_matrix)
# Convert the absolute value matrix to a vector
data_vector <- as.vector(data_matrix_abs)
# Calculate the 90th percentile of the absolute values
ninety_percentile_15 <- quantile(data_vector, 0.9)
# Check and count if any absolute value in column 15
if (any(abs(otu_res$point_est$CAR$B[, 9]) > ninety_percentile_15)) {
count_values_abs_greater_than_10_in_col4 <- count_values_abs_greater_than_10_in_col4 + 1
}
# Check and count if absolute values in rows 5 and 6 of column 15
if (abs(otu_res$point_est$CAR$B[3, 9]) > ninety_percentile & abs(otu_res$point_est$CAR$B[6, 9]) > ninety_percentile){
count_values_abs_greater_than_10_in_rows_8_and_9_col4 <- count_values_abs_greater_than_10_in_rows_8_and_9_col4 + 1
}
# Store the horseshoe analysis results
results_90percent[[i]] <- ninety_percentile_15
}## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## done
##
## Predictors will be centered.
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for beta
##
## Algorithm set to be adapive. Assuming all hyper parameters are the same for Omega's off diagonal entries
##
## Algorithm set to be adaptive. Assuming priors are all the same for Omega's diagonals
##
## Algorithm start...
##
## progress:
##
##
## donepercentage_col4 <- (count_values_abs_greater_than_10_in_col4 / sum(!sapply(results_90percent, is.null))) * 100
percentage_rows_8_and_9_col4 <- (count_values_abs_greater_than_10_in_rows_8_and_9_col4 / sum(!sapply(results_90percent, is.null))) * 100
# Print results
#cat("Percentage of iterations where any abs value > 10 in col 14:", percentage_col14, "%\n")
#cat("Percentage of iterations where abs value is upon the 90 pecent quantile is:", percentage_rows_8_and_9_col4, "%\n")Value of percenatge
cat("Percentage of iterations where abs value is upon the 90 pecent quantile is:", percentage_rows_8_and_9_col4, "%\n")## Percentage of iterations where abs value is upon the 90 pecent quantile is: 46.875 %